Iranian Journal of

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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.

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Background and Objectives: Academic failure is of paramount importance for medical students because it might lead to a decline in scientific level of the community of physicians in the future. This study was conducted to investigate the predictors of academic failure in medical students of Lorestan University of Medical Sciences using classification tree. 
 
Methods: In this cohort study, academic records of all medical students of Lorestan University of Medical Sciences during the academic years of 1999-2008 were selected by census and were followed up until September 2016. Academic failure was defined as having at least one of the components of appropriate grade point average, prolonged graduation, academic probation, dropout, expulsion, and any failure in ccomprehensive exams and the CART classification tree was adopted using the SPSS 22 software to predict it.
 
Results: The cumulative incidence of academic failure was 26.4% and the most prevalent components were prolonged graduation (21.7%) and academic probation (15.0%). The probability of academic failure was 0.449 in subjects taking guest courses, 0.220 in subjects with no history of guest courses admitted to courses with less than 40 students and admission quotas of zone 1 or 3, and 0.456 in subjects with no history of guest courses admitted to courses with more than 40 students and males.
 
Conclusion: With respect to identifying the predictors of academic failure, it is suggested that these students be referred to consulting centers of the university or educational supervisors’ moreover, the regulations of taking guest courses in other universities should be revised.