Objectives: According to health surveys, population growth and total fertility rate (TFR) are decreasing in Iran. The economic and social factors in addition to the changing values and attitudes in the Iranian society have had a major impact on fertility decisions and the actions of families, especially women towards childbearing. This is an important issue for policymakers and many researchers in demography and public health thus the investigation of factors that affect low TFR is considered as a necessity. Materials and Methods: The classification and regression trees (CART) algorithm, as one of the most applicable classification trees, along with logistic regression was applied to model the tendency of 4898 women for childbearing in provinces with a TFR lower than the replacement level in Iran. The secondary data were then analysed by SPSS version 24. 0. Results: Based on these two approaches, it was concluded that despite the CART algorithm, logistic regression suffers from some shortcomings including the difficult interpretation of three levels of interactions while not containing a specific method for handling the outliers. In addition, CART results demonstrated that women’ s children ever born (CEB), age, and opinion had significant impacts on their desire to have a child. The groups encompassing “ 10-39-year-old women with CEB≤ 2” and “ 40-49-year-old women with positive attitudes towards childbearing” desired to have more children while “ women with CEB ≥ 3” showed no tendency for childbearing. Conclusions: In general, the results revealed that adopting policies for changing women’ s views on childbearing and creating the necessary resources for preventing the delays in marriage are regarded as important actions toward altering fertility rates. Another important conclusion is applying the CART algorithm as a convenient method for classifying demographical data.

In this study, the site form index which is the most reliable criterion for evaluation of forest site productivity in uneven-aged and mixed stands was used. For this purpose, random-systematic sampling method was used to locate 105 0.1 ha circular sample plots in beech dominated forests in Tarbiat Modares University research forest. The height and diameter ofFagus orientalis Lipsky trees within each sample plot was recorded along with elevation, azimuth and slope of the ground. Also, at the center of plot, soil samples from first layer (0-10 cm) were taken for analyzing several soil variables. Evaluation of forest site productivity by using classification and regression tree algorithm showed that after pruning the full tree, phosphorus, TRASP, clay and bulk density are effective variables, in order of relative importance, on site form and 62% variations in productivity can be explained by these variables. Using generalized linear model and evaluation criteria such as AIC, RMSE, R2 and adjusted R2, the performance of CART model was assessed. The results showed though CART techniques and the generalized linear model justify the same variability in forest productivity but decision tree technique in terms of AIC and BIC criteria is better than the GLM and as well as this technique is easier to interpret.

Background: The purpose of this study was to determine the influence of some eating habits on body mass index (BMI) using a regression model created via the classification and regression tree method (CART). Methods: The study was conducted using a questionnaire specially developed for the study, evaluated for reliability and validity. In addition to demographics (age and sex), the questions concern the timing of the meals and the type of food consumed. The data contains records for 533 people (322 women and 211 men) aged 18 to 65 years. The survey was conducted in the period 2019-2021 in Stara Zagora, Bulgaria. Data were processed using descriptive statistics, and regression and classification data mining method CART. Results: A CART model with a dependent variable BMI and predictors Sex, Age, Breakfast type, Breakfast time, Lunchtime, Lunch type, Dinner time, Dinner type have been created. The obtained model is statistically significant at a significance level of P<0. 0001 and a coefficient of determination R 2 = 0. 495. The normalized importance of the factors that affect the BMI is as follows: Sex (100%), Age (61. 4%), Lunch type (26. 0%), Lunchtime (18. 8%), Dinner time (13. 9%), and Breakfast type (13. 2%). Women have a lower BMI than men. BMI increases with age. Conclusion: The CART method allows to make a classification by the predictors used and gives opportunities for a more in-depth analysis of the reasons for the increase in BMI. The level of influence of diet and eating habits (type of food, time of consumption) on BMI was determined.

Background and purpose: Understanding of the risk factors for cardiovascular artery disease, which is the leading cause of death worldwide, can lead to essential changes in its etiology, prevalence, and treatment. The aim of this study was to compare the results of logistic regression model and classification and regression tree Analysis (CART) in determining the prognostic factors for coronary artery disease in people living in Mashhad, Iran. Materials and methods: The present case-control study used the cohort data of Mashhad stroke and heart atherosclerotic disorder (MASHAD STUDY), 2009. The prognostic factors for coronary artery disease were determined by CART and Logistic regression models using R and Stata 14. Then, the efficiency of the models was compared by computing the area under the performance characteristic curve (AUC). All patients with coronary artery disease were considered as the case and for each case, three controls were selected. Results: According to Logistic model, prognostic factors for coronary artery disease included age, history of myocardial infarction, diabetes, history of hyperlipidemia, and family history of heart disease (father and brother). The CART algorithm showed age, history of myocardial infarction, history of hypertension, depression, physical activity level, and body mass index as prognostic factors for coronary artery disease in people in Mashhad. Conclusion: Myocardial infarction and age were common prognostic factors for coronary artery disease according to the models applied. According to the efficiency of logistics model, binary multiple logistic regression model is suggested to be used in identifying the factors affecting coronary artery disease, if there is no interaction between the predictors.

Recognition equal units and segregation them and upshot planning per units most basic method for management forest units. Aim this study presentation and comparison classification and regression tree (CART) and random forest (RF) algorithm for forest type mapping using ASTER satellite data in district one didactic and research forest's darabkola. In start using inventory network 500* 350 m, take number 150 sample plat in over district. After accomplish Geometric correction and reduce atmospheric effect on image processing bands rationing, create general vegetation indices, principal component analysis and tesslatcap index. After extraction spectrum values relevant by sample plats fabric and processing bands, classification values other pixel accomplish using investigating algorithms. Evaluation accuracy results classification accomplish by some sample plat that not participate in process classification. The result showed preparation map using RF with overall accuracy 66% and kappa coefficient 0.57 than classification and regression tree with overall accuracy 58% and kappa coefficient 0.49 has superior accuracy. Totality result showed using above algorithm may increased accuracy forest type map.

Background and aims: Risk of implementing invasive diagnostic procedures for coronary artery disease (CAD) such as angiography is considerable. On the other hand, Successful experience has been achieved in medical data mining approaches. Therefore this study has been done to produce a model based on data mining techniques of neural networks that can predict coronary artery disease.Methods: In this descriptive- analytical study, the data set includes nine risk factors of 13228 participants who were undergone angiography at Tehran Heart Center. (4059 participants were not suffering from CAD but 9169 were suffering from CAD). Producing model for predicting coronary artery disease was done based on multilayer perceptron neural networks and variable selection based on classification and regression tree (CART) using of Statistica software. For comparison and selection of best model, the ROC curve analysis was used.Results: After seven-time modeling and comparing the generated models, the final model consists of all existing risk factors obtained with the area under ROC curve of 0.754, accuracy of 74.19%, sensitivity of 92.41% and specificity of 33.25%.Also, variable selection results in producing a model consists of four risk factors with area under ROC curve of 0.737, accuracy of 74.19%, sensitivity of 93.34% and specificity of 31.17% was produced.Conclusion: The obtained model is produced based on neural networks. The model is able to identify both high risk patients and acceptable number of healthy subjects. Also, utilizing the feature selection in this study ends up in production of a model which consists of only four risk factors as: age, sex, diabetes and high blood pressure.

Background and Objectives: Breast cancer is one of the most common malignancies in women which accounts for the highest number of deaths after lung cancer. The aim of the current study was to compare the logistic regression and classification tree models in determining the risk factors and prediction of breast cancer.Methods: We used from the data of a case-control study conducted on 303 patients with breast cancer and 303 controls. In the first step, we included 16 potential risk factors of breast cancer in both the logistic regression and classification tree models. Then, the area under the ROC curve (AUC), sensitivity, and specificity indexes were used for comparing these models.Results: From 16 variables included in the models, 5 variables were statistically significant in both models. Sensitivity, specificity, and AUC was 71%, 69%, and 74.7% for the logistic regression and 63.3%, 68.8%, and 71.1% for the classification tree, respectively.Conclusion: The obtained results suggest that the classification tree has more power for separating patients from healthy people. Menopausal status, number of breast cancer cases in the family, and maternal age at the first live birth were significant indicators in both models.