Search

Decision trees (DTs) are one of the most popular white-box machine-learning techniques. Traditionally, DTs are induced using a top-down greedy search that may lead to sub-optimal solutions. One of the emerging alternatives is an evolutionary induction inspired by the biological evolution. It searches for the tree structure and tests simultaneously, which results in less complex DTs with at least comparable prediction performance. However, the evolutionary search is computationally expensive, and its effective application to big data mining needs algorithmic and technological progress. In this paper, noting that many trees or their parts reappear during the evolution, we propose a reuse strategy. A fixed number of recently processed individuals (DTs) is stored in a so-called repository. A part of the repository entry (related to fitness calculations) is maintained on a CPU side to limit CPU/GPU memory transactions. The rest of the repository entry (tree structures) is located on a GPU side to speed up searching for similar DTs. As the most time-demanding task of the induction is the DTs' evaluation, the GPU first searches similar DTs in the repository for reuse. If it fails, the GPU has to evaluate DT from the ground up. Large artificial and real-life datasets and various repository strategies are tested. Results show that the concept of reusing information from previous generations can accelerate the original GPU-based solution further. It is especially visible for large-scale data. To give an idea of the overall acceleration scale, the proposed solution can process even billions of objects in a few hours on a single GPU workstation. [ABSTRACT FROM AUTHOR]

Highlights • A combination algorithm for ensemble of decision trees is proposed. • RuleCOSI algorithm transforms boosted decision trees into a single ruleset. • The combined rules are simpler than original rules of boosting ensemble. • The performance of the simple production rules was acceptable. Abstract In the field of machine learning, the problem of imbalanced classification arises when the class percentage on the data is unevenly distributed. Different strategies using boosting ensemble algorithms have shown improved results over the imbalanced classification problem by combining weak learners to produce a single strong learner. In particular, decision trees are often used as base learners in ensemble learning for classification or regression. However, boosting ensemble algorithms sometimes generate a large number of decision trees that could grow too large to be understandable and interpretable. Additionally, the use of weights adds more complexity to the final result. For this reason, in this paper, we present RuleCOSI, a novel method for combining and simplifying the output of an ensemble of binary decision trees into a single set of production rules. The proposed method takes into account the weight of each decision tree and using a combination matrix generates a single set of simplified production rules with performance comparable to that of the original boosting ensemble. In order to measure the performance and prove the applicability of the proposed method, we carried out an empirical validation using three different boosting algorithms over several well-known machine learning datasets as well as real-life data collected from a manufacturing company. The results of the algorithm are acceptable in most of the experiments reducing the complexity of the boosting ensemble output while maintaining a similar performance. [ABSTRACT FROM AUTHOR]

In recent years, reinforcement learning (RL) techniques have achieved great success in many different applications. However, their heavy reliance on complex deep neural networks makes most RL models uninterpretable, limiting their application in domains where trust and security are important. To address this challenge, we propose MENS-DT-RL, an algorithm capable of constructing interpretable models for RL via the evolution of decision tree (DT) models. MENS-DT-RL uses a multi-method ensemble algorithm to evolve univariate DTs, guiding the process with a fitness metric that prioritizes interpretability and consistent high performance. Three different initializations for the MENS-DT-RL are proposed, including the use of Imitation Learning (IL) techniques, and a novel pruning approach that reduces solution size without compromising performance. To evaluate the proposed approach, we compare it with other models from the literature on three benchmark tasks from the OpenAI Gym library, as well as on a fertilization problem inspired by real-world crop management. To the best of our knowledge, the proposed scheme is the first to solve the Lunar Lander benchmark with both interpretability and a high confidence rate (90% of episodes are successful), as well as the first to solve the Mountain Car environment with a tree of only 7 nodes. On the real-world task, the proposed MENS-DT-RL is able to produce solutions with the same quality as deep RL policies, with the added bonus of interpretability. We also analyze the best solutions found by the algorithm and show that they are not only interpretable but also diverse in their behavior, empowering the end user with the choice of which model to apply. Overall, the findings show that the proposed approach is capable of producing high-quality transparent models for RL, achieving interpretability without losing performance. • A multi-method ensemble evolutionary algorithm is proposed for Interpretable RL. • Interpretability is achieved by using Decision Trees as RL agents. • Imitation Learning is leveraged to jumpstart the evolutionary algorithm. • A novel method is proposed to prune DTs in RL environments. • State-of-the-art results are obtained for several RL benchmarks. [ABSTRACT FROM AUTHOR]

Decision tree algorithm is an important classification method in data mining techniques. A decision tree creates classification and regression models like a tree that has a root node, branches, and leaf nodes. Logistic regression which is an alternative method to regression analysis when the dependent variable is a dichotomy, is another technique used for classification purposes. Within the scope of this research, logistic regression, linear regression, classification tree, and regression tree were applied on the same data set. This study explores the most important variables determining the house price by using these four methods. Models’ performances and predictive powers were compared and the best model is determined. This comparison was performed using 414 real estate data on 5 independent variables and the dependent variable is house price. The findings showed that the classification tree model for real estate valuation data performs better than standard approaches. [ABSTRACT FROM AUTHOR]

This paper aims to identify the main determinants of the countries that present contagion during the period 2000–2021, based on the determination of the behavior patterns of 18 stock market indices of 15 of the main economies. To do that, first, the B-spline method and Bezier curves are used to smooth observations by minimizing the noise. Subsequently, the Functional Principal Component Analysis (FPCA) methodology is applied. Then, the K-means clustering algorithm is used to determine the main groups using the silhouette method and cross-validation, considering the sum of squares of the distances as the function to minimize. Finally, classification trees and macroeconomic and financial analyses are used to determine the rules of variables that give a direct explanation of the contagion (clustering) between the stock indices. The main empirical results obtained suggest that the most significant macroeconomic variables are the Gross Domestic Product, the Consumer Price Index, and Foreign Direct Investment, while in the financial aspect and the most representative are Domestic Credit and number of companies listed on the stock market. It is worth noticing that government spending does not have a significant effect at any time as a determinant of contagion. Finally, it is important to mention, and surprising, that Mexico's IPC was not clustered in the same group of US stock market indices anytime, despite the strong commercial relationship and the geographical closeness. [ABSTRACT FROM AUTHOR]

Summary: University students in other disciplines without prior knowledge in statistics and/or programming language are introduced to the statistical method of decision trees in the programming language R during a 45‐minute teaching and practice session. Statistics and programming skills are now frequently required within a wide variety of research fields and private industries. However, students unfamiliar with these subjects may be reluctant to join a full course because of time or student workloads or other commitments or a belief it is not for them. The proposed session is short and can be used as an ice‐breaker to let students have a basic understanding of running statistical models in programming language. [ABSTRACT FROM AUTHOR]

Decision trees (DTs) are a widely used prediction tool, owing to their interpretability. Standard learning methods follow a locally optimal approach that trades off prediction performance for computational efficiency. Such methods can however be far from optimal, and it may pay off to spend more computational resources to increase performance. Monte Carlo tree search (MCTS) is an approach to approximate optimal choices in exponentially large search spaces. We propose a DT learning approach based on the Upper Confidence Bound applied to tree (UCT) algorithm, including procedures to expand and explore the space of DTs. To mitigate the computational cost of our method, we employ search pruning strategies that discard some branches of the search tree. The experiments show that proposed approach outperformed the C4.5 algorithm in 20 out of 31 datasets, with statistically significant improvements in the trade‐off between prediction performance and DT complexity. The approach improved locally optimal search for datasets with more than 1,000 instances, or for smaller datasets likely arising from complex distributions. This article is categorized under:Algorithmic Development > Hierarchies and TreesApplication Areas > Data Mining Software ToolsFundamental Concepts of Data and Knowledge > Data Concepts [ABSTRACT FROM AUTHOR]

The article addressed the method for building decision trees paying attention to the binary character of the tree structure. The methodology for building our decision tree for KW-4 igniters was presented. It involves determining features of tested igniters and applied predictors, which are necessary to create the correct model of the tree. The classification tree was built based on the possessed test results, determining the adopted post-diagnostic decision as the qualitative independent variable. The schema of the resultant classification tree and the full structure of this tree together with the results in end nodes were shown. The obtained graphic and tabular sequence of the designed tree was characterized, and the prediction accuracy was evaluated on the basis of the resultant matrix of incorrect classifications. The quality of the resultant predictive model was assessed on the basis of the chosen examples by means of the 'ROC' curve and the graph of the cumulative value of increase coefficient. [ABSTRACT FROM AUTHOR]

Motivation: Industrial policy is on the government agenda in many countries at different stages of development. Despite an extensive academic literature, much of the discussion is too general to guide policy‐makers. Purpose: The article reviews the taxonomies of industrial policy in the literature, highlights some of their limitations and offers a simple alternative based on constraints to industrial development. It applies this framework in the context of one low‐income country —Ethiopia—and suggests a problem‐tree approach to address the binding constraints to growth in particular sectors. Methods: Case studies of Ethiopia's garment, leather goods and cut‐flowers sectors are used as illustrations of the simple taxonomy proposed. Conclusion: In low‐income countries, the key issues for industrial policy often relate to basic issues, such as the provision of adequate infrastructure and access to land, rather than to the choice of particular forms of incentive. Policy Implications: By focusing on different markets, the simple taxonomy proposed could clarify government options in deciding how best to overcome critical constraints to competitiveness in a given sector. [ABSTRACT FROM AUTHOR]

In this paper, we propose a decision-tree modeling in the framework of membrane computing. We propose an algorithm to obtain a P system that is equivalent to any decision tree taken as input. In our case, and unlike previous proposals, we formulate the concepts of decision trees endogenously, since there is no external agent involved in the modeling. The tree structure can be defined naturally by the topology of the regions in the P system and the decision rules are defined by communication rules of the P system. [ABSTRACT FROM AUTHOR]

Decision forests are known to excel in tabular data once their hyperparameters are well-tuned. In addition to being accurate and robust classifiers, these models can be easily converted into a collection of if-else rules that clearly describe the model's decision-making process. However, in practice, decision trees may reach enormous depth and size, and as a result, the collection of rules is vast and complicated. Furthermore, the model may consume a significant amount of memory space on the machine. By creating compact models, it is possible to establish a modest set of rules that may be easier to understand, require less memory, and by nature, may increase the decision-making ability and avoid overfitting. Previous studies attempted to reduce the size of the trees by altering their structure, but this affects both their advantages and simplicity because their structure is much more complicated. (e.g. oblique trees). In this research, we present FACET , a novel algorithm for retaining the compactness of trees while referring to the model as a black box. FACET addresses this by utilizing automated feature engineering methods, which generate a new feature set from the data set by manipulating the current feature set, resulting in a drastic reduction in the size of the decision trees while preserving and even improving the model's accuracy. Our algorithm, FACET, has been extensively tested on multiple datasets, models, and operators to demonstrate its effectiveness. On average, FACET achieves a 24% reduction in the size criteria of the tree-based model without sacrificing accuracy. This reduction in size leads to an average memory reduction of 44% on the dataset required for learning. These statistically significant results demonstrate the potential of FACET to enable more efficient and interpretable tree-based models, without compromising their accuracy, in practical applications. • New algorithm retains decision tree compactness while treating model as black box. • First literature to reduce tree size by modifying user features. • New approach formally defined in paper. • Automated feature engineering methods proposed for problem handling. • Algorithms assessed on diverse data sets. [ABSTRACT FROM AUTHOR]

This study examines the use of data-driven controllers for near real-time control of an HVAC and storage system in a residential building. The work is based on a two-stage management with, first, a day-ahead optimal scheduling, and second, a near real-time adaptive control to remain close to the commitments made in the first stage. A Model Predictive Control (MPC) is adopted from previous works from the authors. The aim of this paper is then to explore lightweight controllers for the real-time stage as alternatives to MPC, which relies on computational-intensive modeling and optimization. Decision Trees (DTs) are considered for this purpose, offering understandable solutions by processing input data through explicit tests of the inputs with predefined thresholds. Various DT variations, including regular, regressors, and linear DTs, are studied. Linear DTs, with a minimal number of leaves, exhibit superior performance, especially when trained on historical MPC data, outperforming the reference MPC in terms of energy exchange efficiency. However, due to impracticalities, an offline training approach for the DTs is proposed, which sacrifices performance. An online tuning strategy is then introduced, updating the DT coefficients based on real-time observations, significantly enhancing performance in terms of energy deviation reduction during real-time operation. [ABSTRACT FROM AUTHOR]

As evidenced in the literature, music has accompanied the human being for millennia, in different situations, emotions and activities. In addition, not only does it allow expressions of internal personal states and feelings, but it can also produce many positive effects in those who practice it. Various authors have explored these benefits that musical activity brings, mainly in children. They highlight positive aspects of learning music in different areas of knowledge, in school performance and even improvements in the IQ of infants. However, despite the large number of studies regarding the benefits of music in children and the different nascent teaching alternatives, in Colombia the situation continues to be dramatic in terms of the incorporation of musical activity in the school curriculum. The foregoing added to political factors, teaching spaces and teacher training. In this way, the present work offers a new musical learning alternative, aimed at children from 7 to 11 years old, through a musical software focused on the initial teaching of the instrumental keyboard. It is important to mention that the software has a feedback system based on decision trees, which allows reinforcing the topics covered in the application. Finally, a comparative analysis is presented between teaching using the software and traditional teaching with the book, through an Investigation-Action carried out over six days with two students from a public school in the city of Bogotá, Colombia. This Investigation-Action allowed to observe positive results based on the comments and performance of the participants, which opens a great possibility for the subsequent scaling of this application. [ABSTRACT FROM AUTHOR]

Background There is no risk and benefit assessment of COVID-19 vaccination for children younger than 5 years using a single health outcomes scale. The objective of this study is to compare the expected risk and benefits of the mRNA primary series of COVID-19 vaccines for children aged 6 months to 4 years in the United States using a single health outcome scale in the Omicron era. Methods The expected benefits and risks of the primary two-dose series of mRNA COVID-19 vaccines for children aged 6 months to 4 years were stratified by sex, the presence of underlying medical conditions, the presence of infection-induced immunity, and the type of mRNA vaccine (BNT162b2 or mRNA-1273). A scoping literature review was conducted to identify the indicators in the decision tree model. The benefit–risk ratio was the outcome of interest. Results The benefit–risk ratios ranged from 200.4 in BNT162b2 for males aged 6–11 months with underlying medical conditions and without infection-induced immunity to 3.2 in mRNA-1273 for females aged 1–4 years without underlying medical conditions and with infection-induced immunity. Conclusions The expected benefit of receiving the primary series of mRNA vaccines outweighed the risk among children ages 6 months to 4 years regardless of sex, presence of underlying medical conditions, presence of infection-induced immunity, or type of mRNA vaccines. However, the continuous monitoring of the COVID-19 epidemiology as well as vaccine effectiveness and safety is important. [ABSTRACT FROM AUTHOR]

Identifying in advance who is unlikely to respond to a specific antidepressant treatment is crucial to precision medicine efforts. The current work leverages genome-wide genetic variation and machine learning to predict response to the antidepressant citalopram using data from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial (n = 1257 with both valid genomic and outcome data). A confirmatory approach selected 11 SNPs previously reported to predict response to escitalopram in a sample different from the current study. A novel exploratory approach selected SNPs from across the genome using nested cross-validation with elastic net logistic regression with a predominantly lasso penalty (alpha = 0.99). SNPs from each approach were combined with baseline clinical predictors and treatment response outcomes were predicted using a stacked ensemble of gradient boosting decision trees. Using pre-treatment clinical and symptom predictors only, out-of-fold prediction of a novel treatment response definition based on STAR*D treatment guidelines was acceptable, AUC =.659, 95% CI [0.629, 0.689]. The inclusion of SNPs using confirmatory or exploratory selection methods did not improve the out-of-fold prediction of treatment response (AUCs were.662, 95% CI [0.632, 0.692] and.655, 95% CI [0.625, 0.685], respectively). A similar pattern of results were observed for the secondary outcomes of the presence or absence of distressing side effects regardless of treatment response and achieving remission or satisfactory partial response, assuming medication tolerance. In the current study, incorporating SNP variation into prognostic models did not enhance the prediction of citalopram response in the STAR*D sample. [ABSTRACT FROM AUTHOR]

For the virtual reality 360 ∘ videos, equirectangular projection (ERP) is a commonly used projection format. However, its high resolution brings extraordinary huge computational complexity in encoding. In order to speed up the intra coding process, a fast coding unit (CU) partitioning algorithm based on regional decision tree is proposed in this paper. The frame image is divided into two regions from a statistical point of view, and the earlysplit and pruned decision trees are established using light weight sample attributes for each region. With the help of these decision trees, the CU partitioning process is accelerated. Compared with the original algorithm of HM16.20, the proposed algorithm can reduce the encoding time by 28%, while BD-rate only increases by 0.27%. [ABSTRACT FROM AUTHOR]

Classifying catalogue objects as stars, galaxies, or active galactic nuclei (AGNs) is a crucial part of any statistical study of galaxies. We describe our pipeline for binary (star/galaxy) and multiclass (star/galaxy/Type I AGN/Type II AGN) classification developed for the very deep CLAUDS + HSC-SSP u * grizy data set. Our method uses the XGBoost implementation of gradient boosted trees (GBTs) to train ensembles of models that take photometry, colours, maximum surface brightnesses, and effective radii from all available bands as input, and output the probability that an object belongs to each of the classes under consideration. At i AB < 25 our binary star/galaxy model has AUC = 0.9974 and at the threshold that maximizes our sample's weighted F1 score, selects a sample of galaxies with 99.7 per cent purity and 99.8 per cent completeness. We test the model's ability to generalize to objects fainter than those seen during training and find that extrapolation of ∼1−2 mag is reasonable for most applications provided that the galaxies in the training sample are representative of the range of redshifts and colours of the galaxies in the target sample. We also perform an exploratory analysis of the method's ability to identify AGNs using a small X-ray-selected sample and find that it holds promise for classifying Type I AGN, although it performs less well for Type II AGN. Our results demonstrate that GBTs provide a flexible, robust, and efficient method for performing classification of catalogue objects in large astronomical imaging surveys. [ABSTRACT FROM AUTHOR]

We consider a generalization of the Pólya–Kasteleyn approach to counting the number of perfect matchings in a graph based on computing the symbolic Pfaffian of a directed adjacency matrix of the graph. Complexity of algorithms based on this approach is related to the complexity of the sign function of a perfect matching in generalized decision tree models. We obtain lower bounds on the complexity of the sign of a perfect matching in terms of the deterministic communication complexity of the XOR sign function of a matching. These bounds demonstrate limitations of the symbolic Pfaffian method for both the general case and the case of sparse graphs. [ABSTRACT FROM AUTHOR]

Background: The social and behavioural factors related to physical activity among adults are well known. Despite the overlapping nature of these factors, few studies have examined how multiple predictors of physical activity interact. This study aimed to identify the relative importance of multiple interacting sociodemographic and work-related factors associated with the daily physical activity patterns of a population-based sample of workers. Methods: Sociodemographic, work, screen time, and health variables were obtained from five, repeated cross-sectional cohorts of workers from the Canadian Health Measures Survey (2007 to 2017). Classification and Regression Tree (CART) modelling was used to identify the discriminators associated with six daily physical activity patterns. The performance of the CART approach was compared to a stepwise multinomial logistic regression model. Results: Among the 8,909 workers analysed, the most important CART discriminators of daily physical activity patterns were age, job skill, and physical strength requirements of the job. Other important factors included participants' sex, educational attainment, fruit/vegetable intake, industry, work hours, marital status, having a child living at home, computer time, and household income. The CART tree had moderate classification accuracy and performed marginally better than the stepwise multinomial logistic regression model. Conclusion: Age and work-related factors–particularly job skill, and physical strength requirements at work–appeared as the most important factors related to physical activity attainment, and differed based on sex, work hours, and industry. Delineating the hierarchy of factors associated with daily physical activity may assist in targeting preventive strategies aimed at promoting physical activity in workers. [ABSTRACT FROM AUTHOR]

Diabetes is one of the most deadly chronic diseases because most sufferers do not realize they have it. A more accurate prediction of diabetes disease must be made to reduce the risk of bad things happening to sufferers. This research will optimize the decision tree (DT) classification method for diabetes prediction. Optimization is done by splitting criteria, splitting data, particle swarm optimization (PSO), and parameter optimization to find the highest and most accurate forecast of diabetes. Splitting criteria is done by comparing the results of three criteria, namely gain ratio (GR), information gain (IG), and gini index (GI). Splitting data is done by dividing training data and testing data into three comparison groups, namely 70:30, 80:20, and 90:10. The application of PSO and parameter optimization is carried out to increase the accuracy value. The processed data is taken from the UCI machine learning repository with 520 records and 17 attributes (1 class/label attribute). From the experiments, the GI criterion with splitting data 90:10 obtained the greatest accuracy of 98.08%, and the combination with PSO resulted in an accuracy of 97.66%. Meanwhile, parameter optimization with splitting data 90:10 combined with GR criteria resulted in the highest accuracy of 97.90%. [ABSTRACT FROM AUTHOR]

Multicriteria analysis is widely used to prove the excellence of prefabricated construction compared with conventional construction. However, because previous studies have not presented the results of an integrated analysis, identifying the merits of prefabricated construction is challenging. Furthermore, clients experience difficulty when considering prefabricated construction owing to the complexity of simulations and the lack of data. Therefore, this study aimed to conduct a multicriteria analysis for prefabricated construction considering productivity, safety, environment, and economy, and develop a multi-prediction model. This study was conducted in five stages. Results revealed that prefabricated construction was superior to conventional construction for all variables, with the former scoring 0.0927 on average and the latter scoring 1.863. The multiprediction model utilizing a decision tree and Bayesian optimization has a high performance, achieving over 94%. Using study findings, decision makers can use the multiprediction model to assess the expected performance of prefabricated construction. This enables a comprehensive comparison of various conditions across different aspects through the multicriteria analysis. [ABSTRACT FROM AUTHOR]

Visualizations are powerful tools that are commonly used by data scientists to get more insights about their high dimensional data. One can for example cite t -SNE, which is probably one of the most famous and widely-used visualization techniques. However, t -SNE is a nonlinear and non-parametric technique that makes it suffer from a lack of interpretability. In this paper, we present a new technique inspired by t -SNE's objective function that combines its ability to build nice visualizations with the interpretability of decision trees. This new visualization technique, called DT-SNE, can be seen as a discrete visualization technique where groups of instances are provided, as well as a ranking between them. The decision rules of the decision tree provide clear insights to interpret these different groups. [ABSTRACT FROM AUTHOR]

Flash-floods are among the natural risk phenomena that annually cause important material damages and losses of human lives worldwide. One of the main activities for mitigating the negative effects of these phenomena consist of the identification and spatial representation of the surfaces prone to surface runoff occurrence. Flash-Flood Potential Index (FFPI) is the common method used to assess the degree of susceptibility to flash-floods of a certain surface. The main drawback of the aforementioned method is represented by the fact that, in the majority of the studies, the geographical factors considered for FFPI calculation received equal weights, even if they do not influence in the same measure the surface runoff process. Moreover, within the methodologies developed in the previous studies, the areas affected by torrential phenomena have not been considered for FFPI computation. To address these shortcomings, in the present study, FFPI values are estimated by using a number of 4 stand-alone models (Alternating Decision Tree (ADT), Rotation Forest, Weights of Evidence (WOE), Logistic Model Tree) and 3 hybrid models generated by the integration of WOE model with each of the other decision tree-based algorithms. The first stage of the study consisted of the inventory of the areas where torrential phenomena occurred in the past, 70% of them being included in the training sample, while the others 30% in the validating sample. Further, 12 flash-flood conditioning factors, selected through the correlation-based feature selection algorithm, were used to train the 7 models applied for FFPI calculation. The results of the 7 models revealed that the surfaces with a high and very high flash-flood susceptibility occupy between 23.3 and 43.7% of the entire study zone. The ROC Curve method was involved in the models performance assessment and in the results validation procedure. From this point of view, the best results were obtained by the ADT-WOE hybrid model. [ABSTRACT FROM AUTHOR]

In this report are used two data sets involving the main antidiabetic enzyme targets α‐amylase and α‐glucosidase. The prediction of α‐amylase and α‐glucosidase inhibitory activity as antidiabetic is carried out using LDA and classification trees (CT). A large data set of 640 compounds for α‐amylase and 1546 compounds in the case of α‐glucosidase are selected to develop the tree model. In the case of CT‐J48 have the better classification model performances for both targets with values above 80%–90% for the training and prediction sets, correspondingly. The best model shows an accuracy higher than 95% for training set; the model was also validated using 10‐fold cross‐validation procedure and through a test set achieving accuracy values of 85.32% and 86.80%, correspondingly. Additionally, the obtained model is compared with other approaches previously published in the international literature showing better results. Finally, we can say that the present results provided a double‐target approach for increasing the estimation of antidiabetic chemicals identification aimed by double‐way workflow in virtual screening pipelines. [ABSTRACT FROM AUTHOR]

Issue of enterprise financial distress represents the actual and interdisciplinary topic for the economic community. The bankrupt is thus one of the major externalities of today's modern economies, which cannot be avoided even with every effort. Where there are investment opportunities, there are individuals and businesses that are willing to assume their financial obligations and the resulting risks to maintain and develop their standard of living or their economic activities. The decision tree algorithm is one of the most intuitive methods of data mining which can be used for financial distress prediction. Systematization literary sources and approaches prove that decision trees represent the part of the innovations in financial management. The main propose of the research is a possibility of application of a decision tree algorithm for the creation of the prediction model, which can be used in economy practice. Paper main aim is to create a comprehensive prediction model of enterprise financial distress based on decision trees, under the conditions of emerging markets. Paper methods are based on the decision tree, with emphasis on algorithm CART. Emerging markets included 17 countries: Slovak Republic, Czech Republic, Poland, Hungary, Romania, Bulgaria, Lithuania, Latvia, Estonia, Slovenia, Croatia, Serbia, Russia, Ukraine, Belarus, Montenegro and Macedonia. Paper research is focused on the possibilities of implementation of decision tree algorithm for creation of prediction model in the condition of emerging markets. Used data contained 2,359,731 enterprises from emerging markets (30% of total amount); divided into prosperous enterprises (1,802,027) and non-prosperous enterprises (557,704); obtained from Amadeus database. Input variables for model represented 24 financial indicators, 3 dummy variables and countries GDP data, in the years 2015 and 2016. The 80% of enterprises represented training sample and 20% test sample, for model creation. The model correctly classified 93.2% of enterprises from both the training and test sample. Correctly classification of non-prosperous enterprises was 83.5% in both samples. The result of the research brings the new model for identification of bankrupt of enterprises. The created prediction model can be considered sufficiently suitable for classifying enterprises in emerging markets. [ABSTRACT FROM AUTHOR]

Use of robust regression algorithms for better prediction of body weight (BW) is receiving increased attention. The present study therefore aimed at predicting BW from chest circumference, breed and sex of a total of 1,012 goats. The animals comprised 332 matured West African Dwarf (WAD) (197 bucks and 135 does), 374 Red Sokoto (RS) (216 bucks and 158 does) and 306 Sahel (SH) (172 bucks and 134 does) randomly selected in Nasarawa State, north central Nigeria. BW prediction was made using automatic linear modeling (ALM), multivariate adaptive regression splines (MARS), classification and regression tree (CART), chi-square automatic interaction detection (CHAID) and exhaustive CHAID. The predictive ability of each statistical approach was measured using goodness of fit criteria i.e. Pearson's correlation coefficient (r), Coefficient of determination (R²), Adjusted coefficient of determination (Adj. R²), Rootmean-square error (RMSE), Mean absolute percentage error (MAPE), Mean absolute deviation (MAD), Global relative approximation error (RAE), Standard deviation ratio (SD ratio), Akaike's information criterion (AIC) and Akaike's information criterion corrected (AICc). Male RS and SH goats had significantly (P<0.05) higher BW and CC compared to their female counterparts while in WAD, male goats had significantly (P<0.05) higher CC (57.88±0.51 vs. 55.45±0.55). CC was determined to be the trait of paramount importance in BW prediction, as expected. Among the five models, MARS algorithm gave the best fit in BW prediction with r, R², Adj. R², SDratio, RMSE, RAE, MAPE, MAD, AIC and AICc values of 0.966, 0.933, 0.932, 0.26, 1.078, 0.045, 3.245, 0.743, 186.0 and 187.0, respectively. The present information may guide the choice of model which may be exploited in the selection and genetic improvement of animals including feed and health management and marketing purposes, and especially in the identification of the studied breed's standards. [ABSTRACT FROM AUTHOR]

The screen content coding (SCC) extension of high efficiency video coding (HEVC) improves coding gain for screen content videos by introducing two new coding modes, namely, intra block copy (IBC) and palette (PLT) modes. However, the coding gain is achieved at the increased cost of computational complexity. In this paper, we propose a decision tree-based framework for fast intra mode decision by investigating various features in the training sets. To avoid the exhaustive mode searching process, a sequential arrangement of decision trees is proposed to check each mode separately by inserting a classifier before checking a mode. As compared with the previous approaches where both IBC and PLT modes are checked for screen content blocks (SCBs), the proposed coding framework is more flexible which facilitates either the IBC or PLT mode to be checked for SCBs such that computational complexity is further reduced. To enhance the accuracy of decision trees, dynamic features are introduced, which reveal the unique intermediate coding information of a coding unit (CU). Then, if all the modes are decided to be skipped for a CU at the last depth level, at least one possible mode is assigned by a CU-type decision tree. Furthermore, a decision tree constraint technique is developed to reduce the rate–distortion performance loss. Compared with the HEVC-SCC reference software SCM-8.3, the proposed algorithm reduces computational complexity by 47.62% on average with a negligible Bjøntegaard delta bitrate (BDBR) increase of 1.42% under all-intra (AI) configurations, which outperforms all the state-of-the-art algorithms in the literature. [ABSTRACT FROM AUTHOR]

Air traffic controllers are responsible for the safe, expeditious and orderly flow of the air traffic. Their training relies heavily on the use of simulators that can represent various normal and emergency situations. Accurate classification of air traffic scenarios can provide assistance towards a better understanding of how controllers respond to the complexity of a traffic scenario. To this end, we conducted a field study using qualified air traffic controllers, who participated in simulator sessions of terminal radar approach control in a variety of scenarios. The aim of the study was twofold, firstly to explore how decision trees and classification rules can be used for realistic classification of air traffic scenarios and secondly to explore which factors reflect better operational complexity. We applied machine learning methods to the data and developed decision trees and classification rules for these scenarios. Results indicated that decision trees and classification rules are useful tools in accurately categorizing scenarios and that complexity requires a larger set of predictors beyond simple aircraft counts. The derived decision trees and classification rules performed well in prediction, stability and interpretability. Practical benefits can be derived in the areas of operations and system design in the context of air traffic flow and capacity management systems. [ABSTRACT FROM AUTHOR]

3D printing has the potential to revolutionize industrial manufacturing through efficient and sustainable techniques. Fused Deposition Modeling (FDM) is a broadly deployed technique among various 3D printing methods. However, the surface quality of FDM is greatly influenced by multiple factors, making it challenging to unravel the relationship between printing quality and parameter settings. To break through this bottleneck, this study proposes an intelligent approach that combines Transfer Learning (TL)-based Feature Extractor (FE) and Gradient-Boosting Decision Trees (GBDT) to investigate the effects of FDM printing parameters on surface quality. Experiments are conducted in the laboratory to validate the effectiveness of the FE-GBDT, which is then compared with the exemplary Machine Learning (ML) algorithms. The results show that our proposed TL model can achieve high precision and accuracy over 0.9900, demonstrating the efficacy of FE-GBDT in deciphering the impact of FDM printing parameters on surface quality. The contribution of each parameter is evaluated and indicates that layer height could dramatically affect the surface quality with an importance score of 0.626. The results provide valuable insights for the 3D printing community, proving that the FE-GBDT approach offers improved generalization, faster training, enhanced feature extraction, addressing data scarcity, and the ability to leverage the strengths of both approaches for superior performance across various tasks. [ABSTRACT FROM AUTHOR]

Highlights • A novel modeling strategy for Decision Trees is proposed. • Interactive basis functions induce oblique and non-linear decision boundaries. • Benchmarking shows potential gains in accuracy and/or parsimony of models. • Empirical examples illustrate the application of the modeling strategy. • Decision charts are introduced to improve the interpretability of models. Abstract Decision Trees (DTs) are a machine learning technique widely used for regression and classification purposes. Conventionally, the decision boundaries of Decision Trees are orthogonal to the features under consideration. A well-known limitation of this is that the algorithm may fail to find optimal partitions, or in some cases any partitions at all, depending on the underlying distribution of the data. To remedy this limitation, several modifications have been proposed that allow for oblique decision boundaries. The objective of this paper is to propose a new strategy for generating flexible decision boundaries by means of interactive basis functions (IBFs). We show how oblique decision boundaries can be obtained as a particular case of IBFs, and in addition how non-linear decision boundaries can be induced. One attractive aspect of the strategy proposed in this paper is that training Decision Trees with IBFs does not require custom software, since the functions can be precalculated for use in any existing implementation of the algorithm. Since the underlying mechanisms remain unchanged there is no substantial computational overhead compared to conventional trees. Furthermore, this also means that IBFs can be used in any extensions of the Decision Tree algorithm, such as evolutionary trees, boosting, and bagging. We conduct a benchmarking exercise to understand under which conditions the use of IBFs can improve model the performance. In addition, we present three empirical applications that illustrate the approach in classification and regression. As part of discussing the empirical applications, we introduce a device called decision charts to facilitate the interpretation of DTs with IBFs. Finally, we conclude the paper by outlining some directions for future research. [ABSTRACT FROM AUTHOR]

Background: To examine the longitudinal association between frailty criteria and depression (DEP) in a large sample of older Europeans using decision tree models, and to examine complex relationships between frailty criteria and DEP symptomatology. Methods: Data come from waves six and eight of the Population Survey of Health, Ageing and Retirement in Europe. DEP was assessed using the EURO-D scale (wave 8) and frailty (wave 6). We included 27,122 people (56.9% women), aged 50 or over. Results: Women indicated a higher rate of DEP (29.0%), as well as a higher prevalence of pre-frailty (21.6%) and frailty (10.8%) than men. For both sexes, fatigue, weight loss, and slowness indicated an increased chance of DEP 5 years later. MPA (moderate physical activity) and grip strength were considered longitudinally protective factors for DEP. The highest prevalence of DEP symptomatology 5 years later was 50.3%, pointing to those with fatigue and slowness. Among women, the highest incidence of DEP was 66.8%, identified through fatigue, slowness, and low MPA. Conclusions: Strategies to reduce frailty and DEP in older European adults may include the creation of policies that encourage the promotion of physical capacity to reach MPA levels, as well as an improvement in muscular strength. [ABSTRACT FROM AUTHOR]

In this paper, we present an algorithm for clustering multidimensional data, which we named TreeKDE. It is based on a tree structure decision associated with the optimization of the one-dimensional kernel density estimator function constructed from the orthogonal projections of the data on the coordinate axes. Among the main features of the proposed algorithm, we highlight the automatic determination of the number of clusters and their insertion in a rectangular region. Comparative numerical experiments are presented to illustrate the performance of the proposed algorithm and the results indicate that the TreeKDE is efficient and competitive when compared to other algorithms from the literature. Features such as simplicity and efficiency make the proposed algorithm an attractive and promising research field, which can be used as a basis for its improvement, and also for the development of new clustering algorithms based on the association between decision tree and kernel density estimator. [ABSTRACT FROM AUTHOR]

Hospital revisits significantly contribute to financial burden. Therefore, developing strategies to reduce hospital revisits is crucial for alleviating the economic impacts. However, this critical issue among peritoneal dialysis (PD) patients has not been explored in previous research. This single-center retrospective study, conducted at Chang Gung Memorial Hospital, Chiayi branch, included 1373 PD patients who visited the emergency room (ER) between Jan 2002 and May 2018. The objective was to predict hospital revisits, categorized into 72-h ER revisits and 14-day readmissions. Of the 1373 patients, 880 patients visiting the ER without subsequent hospital admission were analyzed to predict 72-h ER revisits. The remaining 493 patients, who were admitted to the hospital, were studied to predict 14-day readmissions. Logistic regression and decision tree methods were employed as prediction models. For the 72-h ER revisit study, 880 PD patients had a revisit rate of 14%. Both logistic regression and decision tree models demonstrated a similar performance. Furthermore, the logistic regression model identified coronary heart disease as an important predictor. For 14-day readmissions, 493 PD patients had a readmission rate of 6.1%. The decision tree model outperformed the logistic model with an area under the curve value of 79.4%. Additionally, a high-risk group was identified with a 36.4% readmission rate, comprising individuals aged 41 to 47 years old with a low alanine transaminase level ≤15 units per liter. In conclusion, we present a study using regression and decision tree models to predict hospital revisits in PD patients, aiding physicians in clinical judgment and improving care. [ABSTRACT FROM AUTHOR]

Research has demonstrated concussion likely causes autonomic dysfunction leading to exercise intolerance. To measure improvement in exercise intolerance due to suspected dysautonomia associated with protracted concussion recovery, using objective measurements on a Buffalo Concussion Treadmill Test (BCTT) following participation in a prescribed exercise program. This is a retrospective cohort study of 101 patient charts post-concussion. Exercise intolerance was assessed using a BCTT to identify suspected dysautonomia and an exercise prescription was provided using guidelines for treating concussion-associated exercise intolerance. Patients without symptom improvement and/or inability to achieve 80–85% of age-expected maximum heart rate (HR) without symptom exacerbation received a repeat BCTT. Twelve patient charts met inclusion criteria and were included in data analysis. There were significant improvements from pre-intervention to post-intervention testing in: maximum BCTT stage mean scores (p =.02); maximum HR mean scores (p =.01); prescription HR (RxHR) mean scores (p =.01); and HR delta (HR $$\delta $$ δ) mean scores (p =.00). Maximum stage, HR threshold, RxHR, and newly identified HR delta (HR $$\delta $$ δ) are potential objective measurements of progress for dysautonomia treatment post-concussion. Future studies are indicated to create a tailored protocol in the management of protracted concussion-associated dysautonomia. [ABSTRACT FROM AUTHOR]

To address the limitations of LiDAR dynamic target detection methods, which often require heuristic thresholding, indirect computational assistance, supplementary sensor data, or postdetection, we propose an innovative method based on multidimensional features. Using the differences between the positions and geometric structures of point cloud clusters scanned by the same target in adjacent frame point clouds, the motion states of the point cloud clusters are comprehensively evaluated. To enable the automatic precision pairing of point cloud clusters from adjacent frames of the same target, a double registration algorithm is proposed for point cloud cluster centroids. The iterative closest point (ICP) algorithm is employed for approximate interframe pose estimation during coarse registration. The random sample consensus (RANSAC) and four-parameter transformation algorithms are employed to obtain precise interframe pose relations during fine registration. These processes standardize the coordinate systems of adjacent point clouds and facilitate the association of point cloud clusters from the same target. Based on the paired point cloud cluster, a classification feature system is used to construct the XGBoost decision tree. To enhance the XGBoost training efficiency, a Spearman's rank correlation coefficient-bidirectional search for a dimensionality reduction algorithm is proposed to expedite the optimal classification feature subset construction. After preliminary outcomes are generated by XGBoost, a double Boyer–Moore voting-sliding window algorithm is proposed to refine the final LiDAR dynamic target detection accuracy. To validate the efficacy and efficiency of our method in LiDAR dynamic target detection, an experimental platform is established. Real-world data are collected and pertinent experiments are designed. The experimental results illustrate the soundness of our method. The LiDAR dynamic target correct detection rate is 92.41%, the static target error detection rate is 1.43%, and the detection efficiency is 0.0299 s. Our method exhibits notable advantages over open-source comparative methods, achieving highly efficient and precise LiDAR dynamic target detection. [ABSTRACT FROM AUTHOR]

Background: This study aimed to evaluate the cost-effectiveness of a telehealth coaching intervention to prevent gestational diabetes mellitus (GDM) and to calculate the breakeven point of preventing GDM. Methods: Data to inform the economic evaluation model was sourced directly from the large quaternary hospital in Brisbane, where the Living Well during Pregnancy (LWdP) program was implemented, and further supplemented with literature-based estimates where data had not been directly collected in the trial. A cost-effectiveness model was developed using a decision tree framework to estimate the potential for cost savings and quality of life improvement. A total of 1,315 pregnant women (49% with a BMI 25-29.9, and 51% with a BMI ≥ 30) were included in the analyses. Results: The costs of providing routine care and routine care plus LWdP coaching intervention to pregnant women were calculated to be AUD 20,933 and AUD 20,828, respectively. The effectiveness of the LWdP coaching program (0.894 utility) was slightly higher compared to routine care (0.893). Therefore, the value of the incremental cost-effectiveness ratio (ICER) was negative, and it indicates that the LWdP coaching program is a dominant strategy to prevent GDM in pregnant women. We also performed a probabilistic sensitivity analysis using Monte Carlo simulation through 1,000 simulations. The ICE scatter plot showed that the LWdP coaching intervention was dominant over routine care in 93.60% of the trials using a willingness to pay threshold of AUD 50,000. Conclusion: Findings support consideration by healthcare policy and decision makers of telehealth and broad-reach delivery of structured lifestyle interventions during pregnancy to lower short-term costs associated with GDM to the health system. [ABSTRACT FROM AUTHOR]

The purpose of this study is to determine the effect of toxic leadership perception, leader-member exchange, job stress and emotional exhaustion to predict organizational citizenship behavior of hotel employees. The study is conducted with 623 participants from four and five-star hotel firms in Alanya which is among the top destinations in Turkey. Decision tree analysis is applied to determine the variables that decisive predicting organizational citizenship behavior of the employees. The external validity of the results obtained with the decision tree model is tested with artificial neural networks. The analysis showed that the most significant variable to predict employee organizational citizenship behaviors is leader-member exchange. Findings are discussed in terms of theoretical and practical implications for hospitality literature and sector practitioners. [ABSTRACT FROM AUTHOR]

Due to the high rate of geriatric patient visits, scoring systems are needed to predict increasing mortality rates. In this study, we aimed to investigate the in-hospital mortality prediction power of the National Early Warning Score 2 (NEWS2) and the Laboratory Data Decision Tree Early Warning Score (LDT-EWS), which consists of frequently performed laboratory parameters. We retrospectively analyzed 651 geriatric patients who visited the emergency department (ED), were not discharged on the same day from ED, and were hospitalized. The patients were categorized according to their in-hospital mortality status. The NEWS2 and LDT-EWS values of these patients were calculated and compared on the basis of deceased and living patients. Median (interquartile range [IQR]) NEWS2 and LDT-EWS values of the 127 patients who died were found to be statistically significantly higher than those of the patients who survived (NEWS2: 5 [3–8] vs. 3 [1–5]; p < 0.001; LDT-EWS: 8 [7–10] vs. 6 [5–8]; p < 0.001). In the receiver operating characteristic curve analysis, the NEWS2, LDT-EWS, and NEWS2+LDT-EWS—formed by the sum of the two scoring systems—resulted in 0.717, 0.705, and 0.775 area under curve values, respectively. The NEWS2 and LDT-EWS were found to be valuable for predicting in-hospital mortality in geriatric patients. The power of the NEWS2 to predict in-hospital mortality increased when used with the LDT-EWS. [ABSTRACT FROM AUTHOR]

Simple Summary: The distinction between entirely benign and potentially mis-sampled cases presents a notable challenge in the histological examination of transbronchial lung biopsy (TBLB) specimens derived from pulmonary nodules that lack tumor or atypical cells. Such cases are often categorized as non-diagnostic. This study aims to develop a machine learning-based classifier for TBLB specimens, with a specific focus on analyzing the micro-environmental histological reactions present in TBLB and correlating these changes to either benign or malignant status, and to avoid unnecessary sampling procedures and lead to prompt treatment initiation. Background: When obtaining specimens from pulmonary nodules in TBLB, distinguishing between benign samples and mis-sampling from a tumor presents a challenge. Our objective is to develop a machine-learning-based classifier for TBLB specimens. Methods: Three pathologists assessed six pathological findings, including interface bronchitis/bronchiolitis (IB/B), plasma cell infiltration (PLC), eosinophil infiltration (Eo), lymphoid aggregation (Ly), fibroelastosis (FE), and organizing pneumonia (OP), as potential histologic markers to distinguish between benign and malignant conditions. A total of 251 TBLB cases with defined benign and malignant outcomes based on clinical follow-up were collected and a gradient-boosted decision-tree-based machine learning model (XGBoost) was trained and tested on randomly split training and test sets. Results: Five pathological changes showed independent, mild-to-moderate associations (AUC ranging from 0.58 to 0.75) with benign conditions, with IB/B being the strongest predictor. On the other hand, FE emerged to be the sole indicator of malignant conditions with a mild association (AUC = 0.66). Our model was trained on 200 cases and tested on 51 cases, achieving an AUC of 0.78 for the binary classification of benign vs. malignant on the test set. Conclusion: The machine-learning model developed has the potential to distinguish between benign and malignant conditions in TBLB samples excluding the presence or absence of tumor cells, thereby improving diagnostic accuracy and reducing the burden of repeated sampling procedures for patients. [ABSTRACT FROM AUTHOR]

Extreme response style (ERS), the tendency of participants to select extreme item categories regardless of the item content, has frequently been found to decrease the validity of Likert-type questionnaire results. For this reason, various item response theory (IRT) models have been proposed to model ERS and correct for it. Comparisons of these models are however rare in the literature, especially in the context of cross-cultural comparisons, where ERS is even more relevant due to cultural differences between groups. To remedy this issue, the current article examines two frequently used IRT models that can be estimated using standard software: a multidimensional nominal response model (MNRM) and a IRTree model. Studying conceptual differences between these models reveals that they differ substantially in their conceptualization of ERS. These differences result in different category probabilities between the models. To evaluate the impact of these differences in a multigroup context, a simulation study is conducted. Our results show that when the groups differ in their average ERS, the IRTree model and MNRM can drastically differ in their conclusions about the size and presence of differences in the substantive trait between these groups. An empirical example is given and implications for the future use of both models and the conceptualization of ERS are discussed. [ABSTRACT FROM AUTHOR]

Purpose: This study assessed the influences of different lifestyle on mild cognitive impairment (MCI) progression and established a decision tree prediction model to analyse their predictive significance on MCI progression incidence. Patients and Methods: From October 2015 to February 2020,330 patients with MCI were recruited, and demographic and lifestyle information collected. They were followed up for 19.04 ± 10.227 months. Cognitive function was assessed using the Mini-Mental State Examination Scale every 6 months, and they were divided into MCI stable group and MCI progression group. Results: The Kaplan Meier survival analysis showed an overall cohort survival rate of 33.2%; the annual conversion rate of MCI progression was 20%. Physical exercise, social engagement, high-fat diet, age, napping, and tea drinking were decision tree prediction model nodes. Hobbies were the most important factor for predicting MCI progression. The MCI progression probability rates were: with hobbies 26.829% (44 cases), without hobbies 57.831% (96 cases); for those withot hobbies, with physical exercise 43.077% (28 cases) without physical exercise 72.340% (68 cases); for those without hobbies with physical exercise and social engagement 20.000% (4 cases), without social engagement 53.333% (24 cases); for those without hobbies, physical exercises and social engagement and with nap habits 48.485% (16 cases), without nap habits 66.667% (8 cases). The decision tree prediction model AUC for predicting the MCI progression receiver operating characteristic curve was 0.737 (95% confidence interval: 0.685– 0.785) (75.71% sensitivity, 71.75% specificity, P < 0.001. Conclusion: Hobbies, physical exercise, social engagement, napping, and drinking tea can help prevent MCI progression, while a high-fat diet may exacerbate MCI progression. In this study the rule with the lowest MCI progress probability for those who had hobbies, high-fat diet, and social engagement. And the decision tree model had good prediction efficiency. [ABSTRACT FROM AUTHOR]

Analysis of factors influencing the severity of occupational accidents in manufacturing systems is of vital importance for sustainable manufacturing in a business environment. The aim of this study is to identify occupational health and safety risks in the Turkish textile manufacturing industry, which includes "manufacturing of textiles," "manufacturing of wearing apparel," and "manufacturing of leather and related products". There is no study in the literature that examines the risks related to occupational health and safety in Turkish textile manufacturing based on occupational accident records from 2013 to 2019. To fill this gap in the literature, data-driven modeling is conducted to analyze 139,092 accident records including enterprise information, accident information, injured person information, and accident outcome information, which are obtained from the Ministry of Family, Labor, and Social Services in Türkiye. The result of this study showed that there are 50 injury accident decision rules based on 15 distinct accident predictors, which will support decisions for the efficient use of limited resources, the development of effective accident prevention policies, and the stability of the sector. [ABSTRACT FROM AUTHOR]

Background: Patient safety incidents lead to performance difficulties for nurses when providing nursing practice. This affects work-life balance and causes second and third-victimization. This study predicts factors affecting clinical nurses' work-life balance due to patient safety incidents using classification and regression tree analysis techniques. Methods: This study was a secondary analysis of data from a cohort research project, which used a descriptive survey for data collection. Participants comprised 372 nurses. Data were collected using SurveyMonkey, a mobile-based survey software solution, from January to September 2021. Data included the general characteristics of clinical nurses, second damage, second damage support, third damage, and work-life balance. The specific variables included in the analysis chosen through rigorous Lasso analysis form the foundation for predicting work-life balance. Variables with low explanatory power were excluded, thereafter, the variables selected by Lasso were analyzed with a classification and regression tree model to predict work-life balance. Results: A regression tree was applied to predict work-life balance using seven variables—education level, marital status, position, physical distress, second-victim support, turnover intentions, and absenteeism (selected through Lasso analysis). After pruning, at tree size four, when turnover intentions were < 4.250, physical distress < 2.875, and second-victim support < 2.345, the predicted work-life balance was 3.972. However, when turnover intentions were < 4.250, physical distress < 2.875, and second-victim support ≥ 2.345, then the predicted work-life balance was 2.760. Conclusions: This study's insights offer crucial groundwork for crafting targeted workforce risk management strategies and fostering a conducive organizational culture to mitigate nursing occupational stress, potentially curbing the recurrence of patient safety incidents and improving nursing practice while enhancing patient outcomes. Future research should explore second and third victim experiences across various healthcare settings globally to understand their impact on WLB and patient safety outcomes. [ABSTRACT FROM AUTHOR]

Recently, the rise in student numbers has led to the establishment of many new colleges and universities in China. As a result, there has been a significant increase in data collection on students' athletic skills. To manage these data, educational institutions are implementing information management systems. However, tracking sports results remain challenging since sports information may not always be collected during sports teaching. This work presents a systematic strategy to address this problem by evaluating student athletes' abilities using an Apriori algorithm, decision tree (DT), and association rule. It covers the processes for collecting data, preprocessing, selecting features, and evaluating models. The efficiency of the DT algorithm in solving classification problems, including student achievement analysis, is emphasized. The association rule algorithm is applied to figure out the correlation between students' physical fitness and their involvement in physical education. The Apriori algorithm is introduced to reduce the amount of data needed in merging item sets. Lastly, the overall architecture of the college students' physical fitness analysis system is presented. It covers the insertion of sports test scores, the calculation of total scores, and the application of DT analysis for evaluating student achievements. The process involves standardizing database information, selecting a training instance set, and determining attributes based on information gain. The efficiency of the system is evaluated in terms of accuracy, precision, recall, and F1 score. In comparison with previous works, our recommended system can track and analyze students' athletic capability, fitness, and physical ability to create personalized workout routines and monitor their health in real-time. [ABSTRACT FROM AUTHOR]