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The North China Plain and the agricultural region are crossed by the Shanxi-Beijing natural gas pipeline. Residents in the area use rototillers for planting and harvesting; however, the depth of the rototillers into the ground is greater than the depth of the pipeline, posing a significant threat to the safe operation of the pipeline. Therefore, it is of great significance to study the dynamic response of rotary tillers impacting pipelines to ensure the safe operation of pipelines. This article focuses on the Shanxi-Beijing natural gas pipeline, utilizing finite element simulation software to establish a finite element model for the interaction among the machinery, pipeline, and soil, and analyzing the dynamic response of the pipeline. At the same time, a decision tree model is introduced to classify the damage of pipelines under different working conditions, and the boundary value and importance of each influencing factor on pipeline damage are derived. Considering the actual conditions in the hemp yam planting area, targeted management measures have been proposed to ensure the operational safety of the Shanxi-Beijing natural gas pipeline in this region. [ABSTRACT FROM AUTHOR]

Background: Chondrosarcoma (CHS), a bone malignancy, poses a significant challenge due to its heterogeneous nature and resistance to conventional treatments. There is a clear need for advanced prognostic instruments that can integrate multiple prognostic factors to deliver personalized survival predictions for individual patients. This study aimed to develop a novel prediction tool based on recursive partitioning analysis (RPA) to improve the estimation of overall survival for patients with CHS. Methods: Data from the Surveillance, Epidemiology, and End Results (SEER) database were analyzed, including demographic, clinical, and treatment details of patients diagnosed between 2000 and 2018. Using C5.0 algorithm, decision trees were created to predict survival probabilities at 12, 24, 60, and 120 months. The performance of the models was assessed through confusion scatter plot, accuracy rate, receiver operator characteristic (ROC) curve, and area under ROC curve (AUC). Results: The study identified tumor histology, surgery, age, visceral (brain/liver/lung) metastasis, chemotherapy, tumor grade, and sex as critical predictors. Decision trees revealed distinct patterns for survival prediction at each time point. The models showed high accuracy (82.40%–89.09% in training group, and 82.16%–88.74% in test group) and discriminatory power (AUC: 0.806–0.894 in training group, and 0.808–0.882 in test group) in both training and testing datasets. An interactive web‐based shiny APP (URL: https://yangxg1209.shinyapps.io/chondrosarcoma%5fsurvival%5fprediction/) was developed, simplifying the survival prediction process for clinicians. Conclusions: This study successfully employed RPA to develop a user‐friendly tool for personalized survival predictions in CHS. The decision tree models demonstrated robust predictive capabilities, with the interactive application facilitating clinical decision‐making. Future prospective studies are recommended to validate these findings and further refine the predictive model. [ABSTRACT FROM AUTHOR]

Implementation of information and communication technologies (ICTs) in education is defined as the incorporation of ICTs into teaching and learning activities, both inside and outside the classroom. Despite widely studied, there is still no consensus on how it affects student performance. However, before evaluating this, it is crucial to identify which factors impact students' use of ICT for educational purposes. This understanding can help educational institutions to effectively implement ICT, potentially improving student results. Thus, adapting the conceptual framework proposed by Biagi and Loi (2013) and using the 2018 database of the Program for International Student Assessment (PISA) and a decision tree classification model developed based on CRISP-DM framework, we aim to determine which socio-demographic factors influence students' use of ICT for educational purposes. First, we categorized students according to their use of ICT for educational purposes in two situations: during lessons and outside lessons. Then, we developed a decision tree model to distinguish these categories and find patterns in each group. The model was able to accurately distinguish different levels of ICT adoption and demonstrate that ICT use for entertainment and ICT access at school and at home are among the most influential variables to predict ICT use for educational purposes. Moreover, the model showed that variables related to teaching best practices of Internet utilization at school are not significant predictors of such use. Some results were found to be country-specific, leading to the recommendation that each country adapts the measures to improve ICT use according to its context. [ABSTRACT FROM AUTHOR]

Water plays a pivotal role in socio-economic development in Algeria. However, the overexploitations of groundwater resources, water scarcity, and the proliferation of pollution sources (including industrial and urban effluents, untreated landfills, and chemical fertilizers, etc.) have resulted in substantial groundwater contamination. Preserving water irrigation quality has thus become a primary priority, capturing the attention of both scientists and local authorities. The current study introduces an innovative method to mapping contamination risks, integrating vulnerability assessments, land use patterns (as a sources of pollution), and groundwater overexploitation (represented by the waterhole density) through the implementation of a decision tree model. The resulting risk map illustrates the probability of contamination occurrence in the substantial aquifer on the plateau of Mostaganem. An agricultural region characterized by the intensive nutrients and pesticides use, the significant presence of septic tanks, widespread illegal dumping, and a technical landfill not compliant with environmental standards. The critical situation in the region is exacerbated by excessive groundwater pumping surpassing the aquifer's natural replenishment capacity (with 115 boreholes and 6345 operational wells), especially in a semi-arid climate featuring limited water resources and frequent drought. Vulnerability was evaluated using the DRFTID method, a derivative of the DRASTIC model, considering parameters such as depth to groundwater, recharge, fracture density, slope, nature of the unsaturated zone, and the drainage density. All these parameters are combined with analyses of inter-parameter relationship effects. The results show a spatial distribution into three risk levels (low, medium, and high), with 31.5% designated as high risk, and 56% as medium risk. The validation of this mapping relies on the assessment of physicochemical analyses in samples collected between 2010 and 2020. The results indicate elevated groundwater contamination levels in samples. Chloride exceeded acceptable levels by 100%, nitrate by 71%, calcium by 50%, and sodium by 42%. These elevated concentrations impact electrical conductivity, resulting in highly mineralized water attributed to anthropogenic agricultural pollution and septic tank discharges. High-risk zones align with areas exhibiting elevated nitrate and chloride concentrations. This model, deemed satisfactory, significantly enhances the sustainable management of water resources and irrigated land across various areas. In the long term, it would be beneficial to refine "vulnerability and risk" models by integrating detailed data on land use, groundwater exploitation, and hydrogeological and hydrochemical characteristics. This approach could improve vulnerability accuracy and pollution risk maps, particularly through detailed local data availability. It is also crucial that public authorities support these initiatives by adapting them to local geographical and climatic specificities on a regional and national scale. Finally, these studies have the potential to foster sustainable development at different geographical levels. [ABSTRACT FROM AUTHOR]

Aim: To identify risk factors that associated with the occurrence of venous thromboembolism (VTE) within 30 days after hysterectomy among gynecological malignant tumor patients, and to explore the value of machine learning (ML) models in VTE occurrence prediction. Methods: A total of 1087 patients between January 2019 and January 2022 with gynecological malignant tumors were included in this single‐center retrospective study and were randomly divided into the training dataset (n = 870) and the test dataset (n = 217). Univariate logistic regression analysis was used to identify risk factors that associated with the occurrence of postoperative VTE in the training dataset. Machine learning models (including decision tree (DT) model and logistic regression (LR) model) to predict the occurrence of postoperative VTE were constructed and internally validated. Results: The incidence of developing 30‐day postoperative VTE was 6.0% (65/1087). Age, previous VTE, length of stay (LOS), tumor stage, operative time, surgical approach, lymphadenectomy (LND), intraoperative blood transfusion and gynecologic Caprini (G‐Caprini) score were identified as risk factors for developing postoperative VTE in gynecological malignant tumor patients (p < 0.05). The AUCs of LR model and DT model for predicting VTE were 0.722 and 0.950, respectively. Conclusion: The ML models, especially the DT model, constructed in our study had excellent prediction value and shed light upon its further application in clinic practice. [ABSTRACT FROM AUTHOR]

Background: While the accurate prediction of the overall survival (OS) in patients with submandibular gland cancer (SGC) is paramount for informed therapeutic planning, the development of reliable survival prediction models has been hindered by the rarity of SGC cases. The purpose of this study is to identify key prognostic factors for OS in SGC patients using a large database and construct decision tree models to aid the prediction of survival probabilities in 12, 24, 60 and 120 months. Materials and methods: We performed a retrospective cohort study using the Surveillance, Epidemiology and End Result (SEER) program. Demographic and peri-operative predictor variables were identified. The outcome variables overall survival at 12-, 24-, 60, and 120 months. The C5.0 algorithm was utilized to establish the dichotomous decision tree models, with the depth of tree limited within 4 layers. To evaluate the performances of the novel models, the receiver operator characteristic (ROC) curves were generated, and the metrics such as accuracy rate, and area under ROC curve (AUC) were calculated. Results: A total of 1,705, 1,666, 1,543, and 1,413 SGC patients with a follow up of 12, 24, 60 and 120 months and exact survival status were identified from the SEER database. Predictor variables of age, sex, surgery, radiation, chemotherapy, tumor histology, summary stage, metastasis to distant lymph node, and marital status exerted substantial influence on overall survival. Decision tree models were then developed, incorporating these vital prognostic indicators. Favorable consistency was presented between the predicted and actual survival statuses. For the training dataset, the accuracy rates for the 12-, 24-, 60- and 120-month survival models were 0.866, 0.767, 0.737 and 0.797. Correspondingly, the AUC values were 0.841, 0.756, 0.725, and 0.774 for the same time points. Conclusions: Based on the most important predictor variables identified using the large, SEER database, decision tree models were established that predict OS of SGC patients. The models offer a more exhaustive evaluation of mortality risk and may lead to more personalized treatment strategies. [ABSTRACT FROM AUTHOR]

Abstract Background Many effective therapies for psoriasis are being applied in clinical practice in recent years, however, some patients still can’t achieve satisfied effect even with biologics. Therefore, it is crucial to identify factors associated with the treatment efficacy among psoriasis patients. This study aims to explore factors influencing the treatment efficacy of psoriasis patients based on decision tree model and logistic regression. Methods We implemented an observational study and recruited 512 psoriasis patients in Shanghai Skin Diseases Hospital from 2021 to 2022. We used face-to-face questionnaire interview and physical examination to collect data. Influencing factors of treatment efficacy were analyzed by using logistic regression, and decision tree model based on the CART algorithm. The receiver operator curve (ROC) was plotted for model evaluation and the statistical significance was set at P

Background With the aggravation of population aging in China, the disease spectrum of the population has changed and the coexistence of multiple chronic diseases has become the norm for the health status of the older population in China. Patient delay refers to the behaviour of an individual who fails to seek medical care in a timely manner after becoming unwell for a variety of subjective or objective reasons, resulting in a decrease in the treatment effectiveness and a decrease in the quality of the patient's survival. At present, there are few researches on patient delay and the associated factors for elderly adults with multimorbidity in China. Objective To explore the patient delay and the associated factors for older adults with multimorbidity, so as to provide references to further reduce the incidence of patient delay. Methods Eligible elderly patients attending 27 community health centers in Guangdong Province from September to December 2022 were selected for the study using multi-stage stratified whole cluster random sampling method. A self-designed questionnaire was used to collect patients' general information, disease-related information and delays in seeking medical care. Multivariate Logistic regression analysis and a decision tree model based on the CHAID algorithm were used to analyse the influencing factors of patient delay in older adults with multimorbidity. Results A total of 998 patients were included in the study, of which 243 (24.35%) showed delays in seeking medical care. The multivariate Logistic regression results showed that gender (OR=0.701, 95%CI=0.504-0.977, P=0.036), type of household registration (OR=0.590, 95%CI=0.358-0.973, P=0.039), type of health insurance (OR=2.660, 95%CI=1.764-4.010, P

Jing Liu,1,&ast; Tingting Hu,2,3,&ast; Yulan Guan,2,4 Jinguo Zhai2 1Department of Gynecology, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, 510700, People’s Republic of China; 2School of Nursing, Southern Medical University, Guangzhou, Guangdong, 510515, People’s Republic of China; 3Department of Gynecology, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, 510180, People’s Republic of China; 4Department of Gynecology, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, Guangdong, 510105, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Yulan Guan, Department of Gynecology, Guangdong Provincial Hospital of Chinese Medicine, No. 261 Datong Road, Yuexiu District, Guangzhou, 510105, People’s Republic of China, Tel +8615989117143, Email 15989117143@163.com Jinguo Zhai, Nursing School, Southern Medical University, No. 1023-1063 Sha Tai Nan Road, Baiyun District, Guangzhou, 510515, People’s Republic of China, Tel +8618620287620, Email helenjxzhai@gmail.comPurpose: To explore the risk and protective factors for developing ovarian cancer and construct a risk prediction model.Methods: Information related to patients diagnosed with ovarian cancer on the electronic medical record data platform of three tertiary hospitals in Guangdong Province from May 2018 to September 2023 was collected as the case group. Patients with non-ovarian cancer who attended the clinic during the same period were included in the control group. Logistic regression analysis was used to screen the independent variables and explore the factors associated with the development of ovarian cancer. An ovarian cancer risk prediction model was constructed using a decision tree C4.5 algorithm. The ROC and calibration curves were plotted, and the model was validated.Results: Logistic regression analysis identified independent risk and protective factors for ovarian cancer. The sample size was divided into training and test sets in a ratio of 7:3 for model construction and validation. The AUC of the training and test sets of the decision tree model were 0.961 (95% CI:0.944– 0.978) and 0.902 (95% CI:0.840– 0.964), respectively, and the optimal cut-off values and their coordinates were 0.532 (0.091, 0.957), and 0.474 (0.159, 0.842) respectively. The accuracies of the training and test sets were 93.3% and 84.2%, respectively, and their sensitivities were 95.7% and 84.2%, respectively.Conclusion: The constructed ovarian cancer risk prediction model has good predictive ability, which is conducive to improving the efficiency of early warning of ovarian cancer in high-risk groups.Keywords: ovarian cancer, incidence factors, decision tree model, risk prediction

This study looks how satisfied people are with the Delhi metro, which is crucial for sustainable transportation. The study focuses on security, comfort, travel time components. The research shows how important the quality of service is in public transportation and how it affects what people think and if they would recommend it to others. In the research, 430 passengers surveyed and used statistical analysis to understand what factors influence people's opinions. The study reveals that age, gender, comfort and security are important factors. The study also investigates the alternate PT modes and found that metro fare, seating/standing facilities are important aspects especially for different genders. It's important for the DM to think about how people feel about the cost of using it, the seating, and the different preferences of men and women. Making changes like lowering fares and making the metro more comfortable could make it a better option for everyone in Delhi. [ABSTRACT FROM AUTHOR]

Globally, pulmonary tuberculosis is a significant public health and social problem. We investigated the factors influencing the hospitalization cost of patients with pulmonary tuberculosis and grouped cases based on a decision tree model to provide a reference for enhancing the management of diagnosis-related groups (DRGs) of this disease. The data on the first page of the medical records of patients with the primary diagnosis of pulmonary tuberculosis were extracted from the designated tuberculosis hospital. The influencing factors of hospitalization cost were determined using the Wilcoxon rank sum test and multiple linear stepwise regression analysis, and the included cases were grouped using the chi-squared automated interaction test decision tree model, with these influential factors used as classification nodes. In addition, the included cases were grouped according to the ZJ-DRG grouping scheme piloted in Zhejiang Province, and the differences between the two grouping methods were compared. The length of hospital stay, respiratory failure, sex, and age were the determining factors of the hospitalization cost of patients with pulmonary tuberculosis, and these factors were incorporated into the decision tree model to form eight case combinations. The reduction in variance (RIV) using this grouping method was 60.60%, the heterogeneity between groups was high, the coefficients of variance ranged from 0.29 to 0.47, and the intra-group difference was small. The patients were also divided into four groups based on the ZJ-DRG grouping scheme piloted in Zhejiang Province. The RIV using this grouping method was 55.24, the differences between groups were acceptable, the coefficients of variance were 1.00, 0.61, 0.77, and 0.87, respectively, and the intra-group difference was significant. When the pulmonary tuberculosis cases were grouped according to the duration of hospital stay, respiratory failure, and age, the results were rather reasonable, providing a reference for DRG management and cost control of this disease. [ABSTRACT FROM AUTHOR]

An accurate prognostic assessment is pivotal to adequately inform and individualize follow-up and management of patients with differentiated thyroid cancer (DTC). We aimed to develop a predictive model for recurrent disease in DTC patients treated by surgery and 131I by adopting a decision tree model.Age, sex, histology, T stage, N stage, risk classes, remnant estimation, thyroid-stimulating hormone (TSH), thyroglobulin (Tg), administered 131I activities and post-therapy whole body scintigraphy (PT-WBS) were identified as potential predictors and put into regression algorithm (conditional inference tree, c-tree) to develop a risk stratification model for predicting persistent/recurrent disease over time.The PT-WBS pattern identified a partition of the population into two subgroups (PT-WBS positive or negative for distant metastases). Patients with distant metastases exhibited lower disease-free survival (either structural, DFS-SD, and biochemical, DFS-BD, disease) compared to those without metastases. Meanwhile, the latter were further stratified into three risk subgroups based on their Tg values. Notably, Tg values >63.1 ng/mL predicted a shorter survival time, with increased DFS-SD for Tg values <63.1 and <8.9 ng/mL, respectively. A comparable model was generated for biochemical disease (BD), albeit different DFS were predicted by slightly different Tg cutoff values (41.2 and 8.8 ng/mL) compared to DFS-SD.We developed a simple, accurate and reproducible decision tree model able to provide reliable information on the probability of structurally and/or biochemically persistent/relapsed DTC after a TTA. In turn, the provided information is highly relevant to refine the initial risk stratification, identify patients at higher risk of reduced structural and biochemical DFS, and modulate additional therapies and the relative follow-up. [ABSTRACT FROM AUTHOR]

In order to effectively prevent and control accidents, it is essential to trace back the causes of gas explosions in cities. The DT-AR(decision tree-association rule) algorithm is proposed as a quantitative analysis of gas accident features and causality. First, 210 gas explosion accident investigation reports were taken as samples. The gas accident causation system is divided into three aspects, including environmental factors, management factors and physical factors. Management factors were sorted into organizational-level and individual-level factors from the investigation reports. Second, the CART decision tree model was used to compare location features, organizational causality features, and individual causality features of the piped and bottled gas accidents, and a decision tree model with the gas system fault site as the root node was built to filter the key feature variables. In order to reveal factor correlations and deep-level causation, the Apriori algorithm is used to mine accident association rules. The combinations on the branches of the decision tree are used as constraints to filter the critical causality rule, which improves the efficiency of association rule screening and enhances prediction accuracy. The results demonstrate that the DT-AR algorithm can evaluate the importance of variables, quickly locate effective combinations of factors, and mine the complete causal chain. The association rule is screened based on the constraint of the key element combination of the decision tree, which compensates for the low efficiency of the Apriori algorithm for association rule mining. In addition, the accident-caused excavation results provide an effective path for gas companies, outsourced service companies and administrative departments to implement gas safety chain supervision, which can address the problem of gas accident safety management failures and provide decision support for accident prevention. [ABSTRACT FROM AUTHOR]

This study explores how women employees perceive gender discrimination in the workplace and how data can be used to predict perceived workplace gender discrimination (PWGD). The research team modelled the decision tree that predicted PWGD in South Korea using the Classification and Regression Trees (CART) algorithm and the data from the 7th Korean Women Manage Panel (KWMP). Three types of PWGD trees – wage, promotion, and evaluation – and one synthesised PWGD tree were built to predict and classify PWGD by discrimination type. The research findings suggest that the chief executive officer's (CEO) fairness is the cardinal factor in predicting synthesised PWGD, followed by an employee's exposure to sexual harassment. Whereas the CEO's fairness is the principal factor in predicting PWGD in promotion, the direct supervisor's fairness is the most significant factor in predicting PWGD in evaluation. Perceived disparities in pay between women managers and similarly positioned men colleagues are the critical factor in predicting wage PWGD. Lastly, this paper elaborates on important considerations in PWGD and recommendations for continued inquiry. [ABSTRACT FROM AUTHOR]

CAR-T cell therapy is a revolutionary new treatment for hematological malignancies, but it can also result in significant adverse effects, with cytokine release syndrome (CRS) being the most common and potentially life-threatening. The identification of biomarkers to predict the severity of CRS is crucial to ensure the safety and efficacy of CAR-T therapy. To achieve this goal, we characterized the expression profiles of seven cytokines, four conventional biochemical markers, and five hematological markers prior to and following CAR-T cell infusion. Our results revealed that IL-2, IFN-γ, IL-6, and IL-10 are the key cytokines for predicting severe CRS (sCRS). Notably, IL-2 levels rise at an earlier stage of sCRS and have the potential to serve as the most effective cytokine for promptly detecting the condition’s onset. Furthermore, combining these cytokine biomarkers with hematological factors such as lymphocyte counts can further enhance their predictive performance. Finally, a predictive tree model including lymphocyte counts, IL-2, and IL-6 achieved an accuracy of 85.11% (95% CI = 0.763–0.916) for early prediction of sCRS. The model was validated in an independent cohort and achieved an accuracy of 74.47% (95% CI = 0.597–0.861). This new prediction model has the potential to become an effective tool for assessing the risk of CRS in clinical practice.

Turpan-Hami (Tuha) Basin is in an extremely arid area, surrounded by mountains and desert with sparse vegetation. It has experienced serious desertification over the past 30 years, resulting in ecological environment and socio-economic issues. To reveal the temporal and spatial evolutions of desertification degrees in Tuha Basin, we first constructed an evaluation system for the desertification degree classification using vegetation and soil-related indices; then long-time series Landsat 5 TM/ Landsat8 OLI images and a C5.0 decision tree (DT) model were used to monitor large-scale desertification from 1990 to 2020. We applied a geographic detector to discover the driving forces between desertification changes and natural and human factors. The results demonstrated that the severe desertification (SD) accounted for the largest desertification degree, and the transitions of various desertification degrees mainly occurred between extremely severe desertification (ESD) and SD, which were considered the most active degrees. Only 9.4 % of Tuha Basin showed an expansion trend in desertification degree, with the most significant expansion occurred in 1990–1995. The proportion of 13.6 % desertification showed a reversion trend, with the most significant reversion occurred in 1995–2010. The main driving factors affecting the evolution of desertification degrees were precipitation and temperature, with the average q value higher than 0.95. Human activity factors such as year-end cropland area and population also impacted the patterns of desertification degree, with the q values of 0.9 and 0.7, respectively. The interactive detection enhanced the driving forces and the interaction between precipitation and other factors played a leading role in desertification degree changes. The results provide scientific data and a basis for desertification control and ecological protection in arid areas.

Abstract Background Chondrosarcoma (CHS), a bone malignancy, poses a significant challenge due to its heterogeneous nature and resistance to conventional treatments. There is a clear need for advanced prognostic instruments that can integrate multiple prognostic factors to deliver personalized survival predictions for individual patients. This study aimed to develop a novel prediction tool based on recursive partitioning analysis (RPA) to improve the estimation of overall survival for patients with CHS. Methods Data from the Surveillance, Epidemiology, and End Results (SEER) database were analyzed, including demographic, clinical, and treatment details of patients diagnosed between 2000 and 2018. Using C5.0 algorithm, decision trees were created to predict survival probabilities at 12, 24, 60, and 120 months. The performance of the models was assessed through confusion scatter plot, accuracy rate, receiver operator characteristic (ROC) curve, and area under ROC curve (AUC). Results The study identified tumor histology, surgery, age, visceral (brain/liver/lung) metastasis, chemotherapy, tumor grade, and sex as critical predictors. Decision trees revealed distinct patterns for survival prediction at each time point. The models showed high accuracy (82.40%–89.09% in training group, and 82.16%–88.74% in test group) and discriminatory power (AUC: 0.806–0.894 in training group, and 0.808–0.882 in test group) in both training and testing datasets. An interactive web‐based shiny APP (URL: https://yangxg1209.shinyapps.io/chondrosarcoma_survival_prediction/) was developed, simplifying the survival prediction process for clinicians. Conclusions This study successfully employed RPA to develop a user‐friendly tool for personalized survival predictions in CHS. The decision tree models demonstrated robust predictive capabilities, with the interactive application facilitating clinical decision‐making. Future prospective studies are recommended to validate these findings and further refine the predictive model.

Objective To analyze the cost and effectiveness of different HIV screening strategies based on multi-center HIV residual risk study, so as to provide reference for blood centers to adopt appropriate HIV testing strategies. Methods According to the HIV screening and confirmation of blood donors in three blood centers in Anhui Province, the residual risk of different HIV screening strategies was estimated. A decision tree model was established to analyze the cost-effectiveness differences of three different screening strategies under current domestic policies. Results The residual risk of anti-HIV-1 +2 ELISA, HIV Ag/Ab1+2 ELISA and ELISA+NAT were 1.17×10-6,0.84×10-6 and 0.59×10-6, respectively. According to decision tree model analysis, HIV Ag/Ab1+2 ELISA had a cost-effectiveness advantage over anti-HIV 1+2 ELISA when there was no NAT, but the advantage of HIV Ag/Ab1+2 ELISA disappeared when there was one NAT. The cost of HIV reagents, the cost of HIV treatment and the cost of false positive discarding were sensitive factors of the model. Conclusion In this area, one anti-HIV 1+2 ELISA combined with one NAT has a cost-effectiveness advantage. Blood centers need to confirm and evaluate the ELISA reagents used before conducting HIV screening. Under the premise of ensuring sensitivity, reagent cost and reagent false positive rate are the key factors.

In this paper, we integrated machine learning into the field of quantitative investment and established a set of automatic stock selection and investment timing models. Based on the validity test of factors, a multi-factor stock selection model was established to select stocks with the highest investment value to create a stock pool. By comparing the cumulative returns and the overall market returns of different timing signals over the same time period, both the decision tree and the long short-term memory (LSTM) models had great results. Finally, empirical research was reported to show that it is a good combination to introduce machine learning algorithms into quantitative timing.

This study was carried out based on data mining methods aimed at exploring the medication patterns of Chinese medicine in the treatment of recurrent headaches, thereby providing valuable references for the clinical treatment of this condition. Initially, relevant databases and classical traditional Chinese medicine (TCM) books containing prescriptions for treating recurrent headaches were gathered. Subsequently, various techniques, such as frequency analysis, association rule analysis, cluster analysis, and decision tree analysis, were employed to investigate the prescription patterns of Chinese medicine for recurrent headaches. Following the screening process, a total of 174 prescriptions were obtained, encompassing 227 Chinese medicinal ingredients that were utilized cumulatively 1622 times. The findings indicated that Chinese medicine commonly employed in treating recurrent headaches predominantly fell under the categories of dispersing wind, promoting blood circulation and removing blood stasis, and tonifying deficiency. These medicines often possessed warm and pungent flavors and had an affinity for the liver meridian or spleen meridian. Key Chinese medicine identified included Chuanxiong rhizoma, Angelicae dahuricae radix, Asariradix et rhizoma, Saposhnikoviae radix, and Glycyrrhizae radix et rhizoma. Furthermore, through association rule analysis, a total of 92 combinations of Chinese medicine were identified. Cluster analysis yielded six distinct clusters of prescriptions for treating recurrent headaches. Simultaneously, decision tree analysis demonstrated that Angelicae dahuricae radix emerged as the most influential drug, with the CHAID algorithm exhibiting the highest accuracy rate. This study successfully employed data mining techniques to identify the core Chinese medicine used in treating recurrent headaches. It unveiled potential prescription patterns within these prescriptions and clarified the medication strategies for managing this condition. Consequently, these findings provided valuable insights for clinical diagnosis and treatment of recurrent headaches. [ABSTRACT FROM AUTHOR]

In this paper, we integrated machine learning into the field of quantitative investment and established a set of automatic stock selection and investment timing models. Based on the validity test of factors, a multi-factor stock selection model was established to select stocks with the highest investment value to create a stock pool. By comparing the cumulative returns and the overall market returns of different timing signals over the same time period, both the decision tree and the long short-term memory (LSTM) models had great results. Finally, empirical research was reported to show that it is a good combination to introduce machine learning algorithms into quantitative timing. [ABSTRACT FROM AUTHOR]

Quantitative monitoring and evaluation of mangrove pests can provide a scientific basis for mangrove protection and restoration. The mangroves in the Beilun River Mangrove Reserve of Guangxi Province were taken as the research object, and a model was established to identify the mangroves pests using GF1 satellite data. The results show as follows. The red and near-infrared bands of GF1 satellite data were the preferred bands to judge whether the mangroves are healthy or not. The difference vegetation index (DVI) is the preferred vegetation index to distinguish the healthy mangroves from the pest-infested mangroves, the DVI threshold of pest-infested mangrove for different communities can be determined by training samples. Water body and bare beach can be effectively removed by NDVI and green band with proper thresholds. Then, a decision tree model for pest-infested mangrove recognition was established using DVI, NDVI and green band. The recognition accuracy is satisfactory. The comprehensive index F of four communities including Bruguiera gymnoihiza-Avicennia marina, Kandelia obovate-Avicennia marina, Bruguiera gymnoihiza-Aegiceras corniculatum, Bruguiera gymnoihiza-Avicennia marina + Avicennia marina were higher than 0. 89, moreover, and the accuracy rate A, accuracy rate P, and recall rate R were higher than 0. 85. However, the classification accuracy of Avicennia marina was low with F was only 0. 76. [ABSTRACT FROM AUTHOR]

Economic progress has been founded on environmental pressure by generating all types of environmental harm, such as an increase in greenhouse gases in the atmosphere and significant climate change, which has forced people to reflect. Building a people-oriented, comprehensive, coordinated system to compile experience and lessons assists the government in formulating appropriate policies according to the meaning of the scientific development viewpoint. Based on the above, this research designs Cost–Benefit Decision Tree (CBDT) Algorithm for the economic protection development level using a Decision Tree (DT) and Ensemble Algorithm. In the design of ML Algorithm, this research work initially introduces the Decision Tree model for economic protection evaluation by emphasizing its value as a basic machine-learning framework for fully examining many aspects of economic protection development. Second, it investigates the critical role of Ensemble Algorithms, such as Random Forest in augmenting the capabilities of DT. Third, the paper focuses on the development of a complete Economic Protection Evaluation Index System using the DT Algorithm. To provide a robust, scientifically sound, and practicable framework for evaluating economic protection, this approach integrates scientificity, comparability, systematization, hierarchy, relative decoupling, and data availability principles. Finally, the study constructs the suggested ML Algorithm using the insights from the preceding parts. This algorithm combines the Decision Tree and Random Forest models to generate a single, effective tool for evaluating economic development levels. The experimental findings suggest that the proposed hybrid assessment technique is effective across several dimensions. The proposed CBDT demonstrated its capacity to adapt to changing data circumstances in evaluating low-carbon efficiency using real-world data from 30 major Chinese cities. Furthermore, the algorithm regularly beats rival techniques regarding efficiency improvements, with an average improvement of 12.5% across all cities. Notably, it outperforms other algorithms regarding computational efficiency, with a quicker execution time and an exceptional accuracy rate of 93.2%. The proposed ML Algorithm improves decision-making in the economic protection development level field by effectively balancing cost and benefit considerations. It will improve accuracy and efficiency to outperform existing approaches, thereby advancing economic protection evaluation and decision-making processes in this domain. [ABSTRACT FROM AUTHOR]

Bitcoin was launched over a decade ago and has made an increasing impact on the world's financial order, which has attracted the attention of researchers all over the world. The Bitcoin system runs on a dynamic P2P network, containing tens of thousands of nodes, including reachable nodes and unreachable nodes. In this article, a detection system, BNS (Bitcoin Network Sniffer), which could collect as many Bitcoin nodes as possible is proposed. For reachable nodes, the authors designed an algorithm, BRF (Bitcoin Reachable-Nodes Finding), based on node activity evaluation which reduces the nodes to be detected and greatly shortens the detection time. For unreachable nodes, the authors trained a decision tree model, BUF (Bitcoin Unreachable-Nodes Finding), to identify unreachable nodes based on attribute features from a large number of node addresses. Experiments showed that BNS discovered an average of 1093 more reachable nodes (6.4%) and 662 more unreachable nodes (2.3%) than the well-known website "Bitnodes" per day. It showed better performance in total nodes and efficiency. Based on the experimental results, the authors analyzed the real network size, node "churn", and geographical distribution. [ABSTRACT FROM AUTHOR]

Previous studies have indicated that the prevalence rate of hypertension in adolescents is high, but it has not received much attention and the influencing factors are unclear, especially in Yunnan Province, China. A cluster sampling method was used to investigate 4781 freshmen in a college in Kunming, Yunnan Province from November to December. Demographic and lifestyle data were collected using questionnaires, and height, weight and blood pressure were measured. Decision tree model of hypertension in college students was established by Chi-square automatic interactive detection method. Prevalence of prehypertension of systolic blood pressure (SBP) and diastolic blood pressure (DBP) were detected in 33.9% and 32.1%, respectively. Prevalence of hypertension of SBP and DBP was detected in 1.2% and 7.2%, respectively. The hypertension and prehypertension decision tree of SBP has gender (χ2 = 728.64, p <.001) at the first level and body mass index (BMI) (boys: χ2 = 55.98, p <.001; girls: χ2 = 79.58, p <.001) at the second level. The hypertension and prehypertension decision tree of DBP has gender (χ2 = 381.83, p <.001) at the first level, BMI (boys: χ2 = 40.54, p <.001; girls: χ2 = 48.79, p <.001) at the second level, only children (χ2 = 6.43, p =.04) and red wine consumption (χ2 = 8.17, p =.017) at the third level. The present study suggests that gender, BMI, only children and red wine consumption were the main factors affecting hypertension in college students in southwest border areas of China. Hypertension in Chinese adolescent is generally ignored. This study first reports the prevalence of hypertension in adolescents in Yunnan Province, China. Four thousand seven hundred and eighty-one freshmen were surveyed and height, weight and blood pressure were measured. A decision tree model was used to analyze the predictors of hypertension. The study demonstrated that gender, BMI, only children and red wine predict hypertension in adolescents. [ABSTRACT FROM AUTHOR]

To determine whether the change of serum β-hCG levels between Days 1 and 4 and 48-h pre-treatment increment in β-hCG can early predict treatment failure of single-dose methotrexate (MTX) in tubal ectopic pregnancies (EP), a retrospective study of 1120 ectopic pregnancies treated with a regimen of a single dose of MTX was conducted in the Department of Obstetrics and Gynaecology, Shanghai First Maternity and Infant Hospital. Treatment failure was defined by an obligation to proceed to surgery or have an additional doses of methotrexate.1350 files were reviewed, with 1120 included for final analysis.64% (722/1120) had β-hCG levels increase on Day 4 after MTX treatment, while 36% (398/1120) had β-hCG levels fall. In this cohort, the treatment failure rate with a single dose of MTX was 15.7% (113/722), and the significant features in the logistic regression model of diagnosing the results of MTX treatment were the ratio of Day 1 to Day 48-h pre-treatment β-hCG values (Odds Ratio (OR) 1.221, 95% Confidence interval (CI) 1.159–1.294), the ratio of Day 4 to Day 1 β-hCG serum values (OR 1.098, 95% CI 1.014–1.226), and β-hCG values on Day 1 (OR 1.070, 95% CI 1.016–1.156). The decision tree model was developed by using increment of β-hCG in 48 h before treatment > =19%, the ratio of Day 4 to Day 1 β-hCG serum values > =36%, and β-hCG values on Day 1> =728 mIU/L to predict the failure of MTX treatment. The diagnostic accuracy, sensitivity and specificity in the test group were 97.22%, 100%, and 96.9%, respectively. What is already known on this subject? A decrease of 15% β-hCG levels between Days 4 and 7 is a common protocol for predicting the success of a single-dose methotrexate therapy of an ectopic pregnancy. What do the results of this study add? This clinical study offers the cut-off values points for prediction of single-dose methotrexate treatment failure. What are the implications of these findings for clinical practice and/or further research? We identified the importance of β-hCG increase between Days 1 and 4 and β-hCG increment in 48 h pre-treatment for predicting the failure of single-dose methotrexate therapy. It can be used to aid the clinician to optimise the selection of the most appropriate treatment methods during a follow-up evaluation after MTX treatment. [ABSTRACT FROM AUTHOR]

BACKGROUND: Acute appendicitis in children refers to the acute inflammation of the appendix, which accounts for 20% ∼ 30% of cases of acute abdomen in pediatric surgery. OBJECTIVE: This study aimed to establish a decision tree model of complicated appendicitis in children using appendiceal ultrasound combined with an inflammatory index and evaluated its clinical efficacy in pediatric patients. METHODS: A total of 395 children admitted to the Emergency Department of the Shanghai Children's Hospital from January 2018 to December 2021 and diagnosed with appendicitis by postoperative pathology were retrospectively analyzed. According to the postoperative pathology, the children were divided into a complicated and non-complicated appendicitis group, respectively. Routine laboratory inflammatory indicators, including white blood cell count, N(%), neutrophil (Neu) count, Neu/lymphocyte ratio (NLR), C-reactive protein (CRP), and procalcitonin were collected from the two groups. Collecting data on ultrasound examination of the appendix includes whether the appendix diameter is thickened, whether the echogenicity of the mesenteric rim surrounding the appendix is enhanced, whether there is rich blood supply in the appendix, and whether there are fecaliths in the appendix lumen. The risk factors for complicated appendicitis were screened out by univariate and multivariate logistic regression analyses, the binary logistic regression prediction and decision tree models were established, respectively, and the receiver operating characteristic (ROC) curve was used to verify the accuracy of the two prediction models. RESULTS: Binary logistic regression analysis showed that CRP, NLR, the presence of an appendicolith, and peripheral retina echo enhancement were independent risk factors for complicated appendicitis in children (P < 0.05). The decision tree model had an overall accuracy of 79%, an area under the ROC curve (AUC) of 0.809 (95% confidence interval [CI] 0.780–0.865), and sensitivity and specificity of 71.3% and 77.7%, respectively. The logistic regression model had an overall accuracy of 74.9%, an AUC value of 0.823 (95% CI, 0.765–0.853), a sensitivity value of 80.3%, and a specificity of 71.8%. CONCLUSION: This predictive model, based on ultrasound of the appendix combined with inflammatory markers, provides a useful method to assist pediatric emergency physicians in diagnosing childhood appendicitis. The decision tree model reflected the interaction of various indexes, and the model was simple, intuitive, and effective. [ABSTRACT FROM AUTHOR]

Influenced by some traditional thinking, some college student managers do not recognize the importance of mental health education and fail to effectively integrate mental health education into student management, resulting in poor student management results. This paper proposes an improved optimization scheme for the ID3 algorithm, using the improved C4-5 algorithm to construct a decision tree model, selecting information gain and gain rate as the attribute measure of the model and deciding the split form of the sample. Succeeding pruning operations are used to correct model errors, and the rules are described in the form of TF-THEN. Mental health education is the application of the constructed data model. The effectiveness of student management after mental health education was differentially analyzed across the different identity perspectives of teachers and students. There are significant differences between teachers and students in the dimensions of practice effect, strengths evaluation, and direction of improvement, with p-values less than 0.05. In the study of the effectiveness of student management on students’ mental health, after the implementation of student management work, there are significant differences between the experimental group of students and the control group in the dimensions of self-confidence, emotional intelligence, and frustration tolerance, with a difference of 4.61, 1.6, and 7.9 points, respectively. There is a reciprocal interactive relationship between student management and mental health education.

Several incidents of electric vehicle combustion across various regions in China have brought the safety concerns associated with new energy vehicles into sharp focus within public discourse. Addressing these concerns, this paper explores maintenance decision-making for new energy vehicles through the application of decision trees and machine learning techniques. Initially, the study analyzes how decision trees and machine learning are employed in crafting maintenance decisions for these vehicles. It involves collecting data through internet searches, followed by statistical analyses and preprocessing to set the groundwork for further inquiry. Furthermore, the research advances by developing and refining decision tree models, which facilitate the integration of fault diagnosis and maintenance decision-making processes for new energy vehicles. This effort culminates in the establishment of a robust decision tree model specifically designed for the maintenance of new energy vehicles, which is subsequently evaluated through a detailed case study. The results are presented: the approximation degree of new energy vehicle fault diagnosis based on the decision tree model is 90.58%, 90.67%, 88.09%, 91.28%, and 90.19% at the significant level of α taking the value of 0.01, 0.05, 0.100, respectively. This study provides theoretical guidance on the application of decision trees for the diagnosis and repair of faults in new energy vehicles so that the development of new energy vehicles in China can be ensured.

The study used cluster analysis and decision tree modeling to construct a multivariate data fusion physical fitness evaluation model, and also built a physical fitness core literacy evaluation model through factor analysis and fuzzy comprehensive evaluation method. These two evaluation models were subsequently applied to physical education and health teaching in colleges and universities to measure the quality of physical education teaching in colleges and universities and students’ physical health status. By analyzing the practice of physical education teaching in colleges and universities, it was found that the pull-up program had the worst performance in students’ physical fitness test, with a score of 287. In addition, the lung capacity and 50-meter dash programs contributed the most to students’ total performance, with scores of 1,291 and 1,245, respectively. From the perspective of physical education and health teaching quality, the scores of teaching situation, teaching management and biological education school effectiveness were 4.68, 4.01 and 4.52, respectively, and all of these evaluation grades were good. This study provided an accurate and scientific analysis of the overall quality of physical education and health teaching in universities and the health status of students.

Currently, college students are facing severe employment challenges. To address this, this study uses mental health education assessment data to explore how to optimize the employment guidance and management of college students in order to enhance their employability and psychological adaptability. Decision tree algorithm and improved K-means clustering algorithm are used to analyze the mental health assessment data in depth. Data mining techniques were used to identify students’ employment tendencies and psychological characteristics so as to develop personalized employment guidance strategies. It was found that the accuracy and effectiveness of career guidance can be effectively enhanced through a data-driven approach. Specifically, the accuracy of predicting employment tendency using the decision tree model reaches 85%, while the application of the improved K-means algorithm for clustering analysis of students’ psychological characteristics enables more precise customization of employment guidance strategies. Comprehensive data analysis and psychological assessment can significantly improve the quality of employment guidance for college students. Colleges and universities should pay attention to the use of mental health assessment data, combined with data mining technology, to optimize career guidance and management, in order to help students better adapt to the workplace and enhance their employment competitiveness.

The teaching of English courses in colleges and universities relies too much on grades to judge students’ mastery of knowledge points, and there are problems of slow feedback and difficulty in timely targeted teaching. To address the drawbacks of traditional English teaching methods, this paper constructs a random forest algorithm based on the decision tree model, uses the information gain index to judge the feature segmentation effect of the random forest, and calculates the degree of students’ mastery of knowledge points. At the same time, the recommendation model is utilized to match students with topics equivalent to their mastery level, and the alternating least squares method is introduced to improve its recommendation efficiency, thus constructing an efficient English teaching course assistance model. The model was implemented in colleges and universities after it had been designed. The highest similarity match of the educational resource ontology tree was achieved when the weights of knowledge points, resource difficulty, and resource type were 1.121, 0.986, and 1.129, respectively. The average score of the class was 65.3 before the application, after the application, the second test score increased to 69.28, which was a significant improvement, and the fourth test was 75.32, which exceeded the average score of 72.23. The investigation of this study shows the direction for the innovation of the English curriculum and promotes the benign development of English teaching.

The development of the data era has put higher requirements for college art courses, but the imperfect evaluation system of practical teaching of college art courses has become a stumbling block to the flourishing development of college art. Based on the decision tree model, this paper proposes constructing the optimal decision tree using pruning techniques. The optimized decision tree is adopted to explore the differences in the construction of art majors in universities under the condition of normal major certification and feedback on the influence of normal major certification on art majors through the evaluation results. From the analysis of the three index examples, the mean values of cognitive evaluation, pedagogical evaluation, and effectiveness evaluation of art courses were 2.527, 4.756, and 3.267, respectively, indicating the best evaluation in the pedagogical evaluation of art courses, but the cognitive evaluation was slightly lower. Normal major certification significantly influences the construction of art majors in colleges and universities. The decision tree model can also guide the innovation direction of college art courses more intuitively through data so that the evaluation system of college art courses can enjoy the development dividend of the new technology era.

The key to the enterprise is in management, and the focus of financial management is enterprise accounting, and the enterprise accounting management system is the core of financial management. Developing a set of accounting management systems has become an urgent problem to solve to improve the efficiency and accuracy of enterprise accounting. In this paper, we use the decision tree model to design the enterprise accounting system and establish an efficient accounting system, which is logically divided into 5 layers: database, data storage layer, business logic layer, business appearance layer and user interface layer. The effectiveness and applicability of the decision tree model for enterprise accounting systems are discussed. The results show that the decision tree model can improve the accounting error tolerance and efficiency and reduce accounting risk. The satisfaction rate of most evaluation indexes has increased to more than 50%, the satisfaction rate of accounting business operation compliance has increased from 6% to 55%, the reasonableness of the internal audit cycle has increased from 18% to 70%, and the timeliness of annual accounting statement disclosure has increased from 8% to more than 50%. The decision tree model is positive for using enterprise accounting and can meet the requirements of enterprise accounting system design.

In this paper, the original two-level planning problem is transformed into a single-level optimization problem by combining the penalty function method for the large amount of data processing involved in the training process of the decision tree model, setting the output as a classification tree in the iterative process of the CART decision tree, and recursively building the CART classification tree with the training set to find the optimal solution set for the nonlinear two-level planning problem. It is verified that the proposed solution method is also stable at a convergence index of 1.0 with a maximum accuracy of 95.37%, which can provide an efficient solution method for nonlinear two-level programming problems oriented to decision tree models.

AbstractPulmonary sarcoidosis is a complex inflammatory disease characterized by granulomas in the lung tissue, leading to breathing difficulties and chest pain. Its etiology remains not fully understood, with factors such as allergies, autoimmune responses and genetics playing a role. This study explores the potential of blood microbiome dysbiosis, defined as an imbalance in the microbial ecosystem, as a missing piece of the puzzle in understanding the etiology of the disease. Our objective was to apply a decision-tree supervised machine learning hierarchical model to distinguish potential patterns of microbiome dysbiosis in blood samples from patients with pulmonary sarcoidosis as compared to healthy age-matched controls. Blood microbiome analysis, being individually-specific and stable, offers a unique perspective. Utilizing 16S rRNA gene amplicon sequencing, we analyzed the blood microbiome composition characterized by non-normally distributed and sparse data. Because of the rarity of the disease in Bulgaria, we studied a relatively small patient group, n = 7. The findings were compared to 21 healthy age-matched controls. Bioinformatics and statistical analysis play a pivotal role in microbiome analysis, especially when discerning associations between taxonomic composition and disorders such as pulmonary sarcoidosis. By analyzing the microbial diversity, we identified alterations in the blood microbiome composition between healthy individuals and those with sarcoidosis, which potentially may trigger the disease. Advanced machine learning techniques provided additional power to the analysis, that might be overlooked by the usual group statistics, confirming the differentiation of the diversity within the studied microbiome.

Purpose Previous studies have indicated that the prevalence rate of hypertension in adolescents is high, but it has not received much attention and the influencing factors are unclear, especially in Yunnan Province, China. Materials and methods A cluster sampling method was used to investigate 4781 freshmen in a college in Kunming, Yunnan Province from November to December. Demographic and lifestyle data were collected using questionnaires, and height, weight and blood pressure were measured. Decision tree model of hypertension in college students was established by Chi-square automatic interactive detection method. Results Prevalence of prehypertension of systolic blood pressure (SBP) and diastolic blood pressure (DBP) were detected in 33.9% and 32.1%, respectively. Prevalence of hypertension of SBP and DBP was detected in 1.2% and 7.2%, respectively. The hypertension and prehypertension decision tree of SBP has gender (χ2 = 728.64, p

To determine whether the change of serum β-hCG levels between Days 1 and 4 and 48-h pre-treatment increment in β-hCG can early predict treatment failure of single-dose methotrexate (MTX) in tubal ectopic pregnancies (EP), a retrospective study of 1120 ectopic pregnancies treated with a regimen of a single dose of MTX was conducted in the Department of Obstetrics and Gynaecology, Shanghai First Maternity and Infant Hospital. Treatment failure was defined by an obligation to proceed to surgery or have an additional doses of methotrexate.1350 files were reviewed, with 1120 included for final analysis .64% (722/1120) had β-hCG levels increase on Day 4 after MTX treatment, while 36% (398/1120) had β-hCG levels fall. In this cohort, the treatment failure rate with a single dose of MTX was 15.7% (113/722), and the significant features in the logistic regression model of diagnosing the results of MTX treatment were the ratio of Day 1 to Day 48-h pre-treatment β-hCG values (Odds Ratio (OR) 1.221, 95% Confidence interval (CI) 1.159–1.294), the ratio of Day 4 to Day 1 β-hCG serum values (OR 1.098, 95% CI 1.014–1.226), and β-hCG values on Day 1 (OR 1.070, 95% CI 1.016–1.156). The decision tree model was developed by using increment of β-hCG in 48 h before treatment > =19%, the ratio of Day 4 to Day 1 β-hCG serum values > =36%, and β-hCG values on Day 1> =728 mIU/L to predict the failure of MTX treatment. The diagnostic accuracy, sensitivity and specificity in the test group were 97.22%, 100%, and 96.9%, respectively.IMPACT STATEMENT What is already known on this subject? A decrease of 15% β-hCG levels between Days 4 and 7 is a common protocol for predicting the success of a single-dose methotrexate therapy of an ectopic pregnancy. What do the results of this study add? This clinical study offers the cut-off values points for prediction of single-dose methotrexate treatment failure. What are the implications of these findings for clinical practice and/or further research? We identified the importance of β-hCG increase between Days 1 and 4 and β-hCG increment in 48 h pre-treatment for predicting the failure of single-dose methotrexate therapy. It can be used to aid the clinician to optimise the selection of the most appropriate treatment methods during a follow-up evaluation after MTX treatment.

The discrete choice modeling and decisions-tree technique are used to understand the travel behavior of people in Budapest. The discrete choice modeling is applied to develop transport mode choice that mimics the travel behavior of people using their personal and travel characteristics. A revealed preference (RP) survey was conducted by the Hungarian Census Bureau which contains information about the households in Budapest in 2014, is used. Understanding the daily main trips of people is firstly analyzed using decisions-tree technique, where the impact of each variable is presented based on its importance in affecting the travel choice mode. In random utility theory, travelers choose one option out of certain available options to them to maximize their utility. The Multinomial Logit (MNL) model is used to examine the relationship between various variables connected to travelers in order to understand the travel behavior pattern. The result of the analysis shows a clear pattern between car ownership and each of family size, and age, and trip cost variables. The result of decision tree analysis demonstrates that travelers’ trip duration is the most important factor that has impact on their transport mode choice, which is mainly distributed within private cars, public transportation, and walking. The developed multinomial transport mode choice model includes sociodemographic, economic, and travel characteristics. The trip time, trip cost, age, car ownership index, trip purpose, gender, employment, and income are the main determinants the impact the transport choice mode. The developed models (i.e., decision tree and the multinomial transport logit choice) are beneficial for the decision-makers who can used it in predicting the travel demand.

Objective To analyze the influence factors of active tuberculosis (ATB) using logistic regression model and decision tree model, and to provide a reference point for ATB prevention and control. Methods The experimental group consisted of 200 active pulmonary tuberculosis patients admitted to the Third People’s Hospital of Kunming from March 2021 to March 2023, and the control group consisted of 200 healthy individuals who underwent physical examination during the same period Logistic regression. Decision tree ATB risk prediction models were established, and the decision tree analysis models (Decision Tree 1 and Decision Tree 2) were set up under the condition of whether they were based on the results of Logistic regression or not, and the decision tree analysis models (Decision Tree 1 and Decision Tree 2) were set up. The prediction effect of the three models was evaluated using the subjects' work curves. Results　The results of Logistic regression showed that AAT, IL-4, IL-6, IL- 17 and IFN-γ were the risk factors for ATB with CD+4 as the protective factor. The results of Decision Tree 1 showed that CRP was the root node, followed by IL-1, IL-6, CD+4, IL-17, AGP, and IFN-γ as the subnodes, respectively. Decision Tree 2 showed that IL-6 was the root node, followed by AAT, IL-4, IL-17 as subnodes. The established risk prediction models showed an AUC of 0.887 for logistic regression and 0.900 (Decision Tree 1) and 0.857 (Decision Tree 2) for the decision tree model. The AUC comparison results of the three models showed that the AUC of Decision Tree 1 was better than that of Decision Tree 2 (95% CI：0.001 9-0.084 1, P < 0.05), but the difference with Logistic regression model was not statistically significant (95% CI：0.026 5-0.052 2, P = 0.526). Conclusion Both Logistic model and Decision Tree 1 model have certain application value in predicting the risk factors of ATB, and it is recommended to combine the two models so as to provide the better reference value for the laboratory prediction of ATB. [ABSTRACT FROM AUTHOR]

The domesticated silkworm, Bombyx mori Linnaeus (Lepidoptera: Bombycidae), often poses a challenge in strain identification due to similarities in morphology and genetic background. In South Korea, around 40 silkworm strains are classified as premium, including 5 endemic tri-molting strains: Goryeosammyeon, Sammyeonhonghoeback, Hansammyeon, Sun7ho, and Sandongsammyeon. These strains have potential for breeding programs in response to emerging industry demands, necessitating a reliable strain identification method. In this study, we established a molecular diagnosis approach for these 5 strains. We selected 2–4 single-nucleotide polymorphisms (SNPs) for each strain from whole-genome sequences of 39 strains, encompassing 37 previously studied and 2 newly added. These SNPs were utilized to construct decision trees for each endemic strain identification. The SNPs can be used to distinguish each target strain from the 38 nontarget strains by the tetra-primer amplification refractory mutation system-polymerase chain reaction, with the exception of HMS which needs the addition of PCR-restriction fragment length polymorphism method at the final step. This decision tree-based method using genomic SNPs, coupled with the 2 typing methods, produced consistent and accurate results, providing 100% accuracy. Additionally, the significant number of remaining SNPs identified in this study could be valuable for future diagnosis of the other strains. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries are widely used in electric vehicles, smartphones, and energy storage devices due to their high power and light weight. The goal of this study is to predict the remaining capacity of a lithium-ion battery and evaluate its performance through three machine learning models: linear regression, decision tree, and random forest, and two deep learning models: neural network and ensemble model. Mean squared error (MSE), mean absolute error (MAE), coefficient of determination (R-squared), and root mean squared error (RMSE) were used to measure prediction accuracy. For the evaluation of the artificial intelligence model, the dataset was downloaded and integrated with measurement data of the CS2 lithium-ion battery provided by the University of Maryland College of Engineering. As a result of the study, the RMSE of the linear regression model was 0.045, the decision tree model was 0.038, the random forest model was 0.034, the neural network model was 0.032, and the ensemble model was 0.030. According to the measured values, the ensemble model showed the best predictive performance, followed by the neural network model. Decision tree and random forest models also showed very good performance, and the linear regression model showed relatively poor predictive performance compared to the other models. [ABSTRACT FROM AUTHOR]

Background: The severity of liver cirrhosis in hepatocellular carcinoma (HCC) patients is essential for determining the scope of surgical resection. It also affects the long-term efficacy of systemic anti-tumor therapy and transcatheter arterial chemoembolization (TACE). Non-invasive tools, including aspartate aminotransferase to platelet ratio index (APRI), fibrosis-4 (FIB-4), and 1-glutamyl transferase to platelet ratio (GPR), are less accurate in predicting cirrhosis in HCC patients. We aimed to build a novel decision tree model to improve diagnostic accuracy of liver cirrhosis. Patients and Methods: The Mann-Whitney U test, 2 test, and multivariate logistic regression analysis were used to identify independent cirrhosis predictors. A decision tree model was developed using machine learning algorithms in a training cohort of 141 HCC patients. Internal validation was conducted in 99 HCC patients. The diagnostic accuracy and calibration of the established model were evaluated using receiver operating characteristic (ROC) and calibration curves, respectively. Results: Sex and platelet count were identified as independent cirrhosis predictors. A decision tree model integrating imaging-reported cirrhosis, APRI, FIB-4, and GPR was established. The novel model had an excellent diagnostic performance in the training and validation cohorts, with area under the curve (AUC) values of 0.853 and 0.817, respectively. Calibration curves and the Hosmer-Lemeshow test showed good calibration of the novel model. The decision curve analysis (DCA) indicated that the decision tree model could provide a larger net benefit to predict liver cirrhosis. Conclusion: Our developed decision tree model could successfully predict liver cirrhosis in HCC patients, which may be helpful in clinical decision-making. [ABSTRACT FROM AUTHOR]