Search

Background: Epithelial ovarian cancer is one of the leading causes of death from gynecological cancers in the Western world. One of the important objectives of medical research is to determine predictors of an event. Regarding the interaction of risk factors, regression methods are unsuitable when the number of factors is high. Objectives: Regarding frequency predictors of recurrence-free survival in epithelial ovarian cancer, our aim in this article is to determine predictors and time to first recurrence using a classification and regression tree model. Methods: This retrospective analysis used medical and chemotherapy records of 141 patients with epithelial ovarian cancer between 2007 and 2018. They were referred to Imam Hossein Hospital in Tehran. Data were analyzed using classification and regression trees in Rver3.4.3. Results: The regression tree results showed that the worst recurrence-free survival in metastatic patients was in grade II patients (15.03 ± 11 months), but in patients without metastases were in patients with CA125 tumor marker above 207 that used 3-week chemotherapy courses (14.53 ± 6.4 months). The classification tree also showed that the most probability of the first recurrence in metastatic patients was in patients with adjuvant chemotherapy (0.81), and patients without metastases were among those with stages 2, 3, and 4 with the maximum platelet count above 305,000 and less than 35 years old (0.75). Conclusions: The classification and regression tree models, without any assumptions, can estimate the probability of recurrence in different subgroups. These models can be used in deciding due to the ease of interpretation by physicians and paramedic [ABSTRACT FROM AUTHOR]

Despite the high level of interpretability of the Classification and Regression Tree (CART) as one of the widely-used data mining models, its results usually are less accurate than other models. To address the low accuracy of the CART model, this paper developed two new and enhanced CART-based algorithms that can provide both high interpretability and high accuracy for flood susceptibility mapping. In the developed models, various hyperparameters of the CART were explicitly tuned using the genetic algorithm (GA) and grid search (GS) methods. Eleven variables and 222 flood locations were used in Sardabroud watershed in Iran for modelling. The results showed area under the receiver operating characteristic curve (AUROC) values of 0.884, 0.927 and 0.908, respectively, for the CART, CART-GS and CART-GA models in the validation run. The results showed that the developed models can produce the output that satisfies both accuracy and interpretability criteria. [ABSTRACT FROM AUTHOR]

Forest canopy cover (FCC) is one of the most important forest inventory parameters and plays a critical role in evaluating forest functions. This study examines the potential of integrating Sentinel-1 (S-1) and Sentinel-2 (S-2) data to map FCC in the heterogeneous Mediterranean oak forests of western Iran in different data densities (one-year datasets vs. three-year datasets). This study used very high-resolution satellite images from Google Earth, gridded points, and field inventory plots to generate a reference dataset. Based on it, four FCC classes were defined, namely non-forest, sparse forest (FCC = 1–30%), medium-density forest (FCC = 31–60%), and dense forest (FCC > 60%). In this study, three machine learning (ML) models, including Random Forest (RF), Support Vector Machine (SVM), and Classification and Regression Tree (CART), were used in the Google Earth Engine and their performance was compared for classification. Results showed that the SVM produced the highest accuracy on FCC mapping. The three-year time series increased the ability of all ML models to classify FCC classes, in particular the sparse forest class, which was not distinguished well by the one-year dataset. Class-level accuracy assessment results showed a remarkable increase in F-1 scores for sparse forest classification by integrating S-1 and S-2 (10.4% to 18.2% increased for the CART and SVM ML models, respectively). In conclusion, the synergetic use of S-1 and S-2 spectral temporal metrics improved the classification accuracy compared to that obtained using only S-2. The study relied on open data and freely available tools and can be integrated into national monitoring systems of FCC in Mediterranean oak forests of Iran and neighboring countries with similar forest attributes. [ABSTRACT FROM AUTHOR]

Land subsidence occurrence in the Tasuj plane is becoming more frequent and hazardous in the near future due to the water crisis. To mitigate damage caused by land subsidence events, it is necessary to determine the susceptible or prone areas. This study focuses on producing and comparing land subsidence susceptibility map (LSSM) using boosted regression tree (BRT), random forest (RF), and classification and regression tree (CART) approaches with twelve influencing variables, namely altitude, slope angle, aspect, groundwater level, groundwater level change, land cover, lithology, distance to fault, distance to stream, stream power index, topographic wetness index, and plan curvature. Moreover, by implementing the Relief-F feature selection method, the most important variables in LSSM procedure were identified. The performance of the adopted methods was assessed using the area under the receiver operating characteristics curve (AUROC) and statistical evaluation indexes. The results showed that all the employed methods performed well; in particular, the BRT model (AUROC = 0.819) yielded higher prediction accuracy than RF (AUROC = 0.798) and CART (AUROC = 0.764). Findings of this study can assist in characterizing and mitigating the related hazard of land subsidence events. [ABSTRACT FROM AUTHOR]