Applications of Hierarchical Classification Techniques for Classifying Anxiety Among Undergraduate Admission Candidates in Bangladesh

Background The undergraduate entrance exam, which is required for admission to either Bangladesh's public higher education institutions or medical institutions, is one of among the most important investigations in a student's life. The purpose of the current research was to employ sophisticated machine learning techniques to determine clinical anxiety prevalence among Bangladeshi admission participants while additionally discovering associated risks. Methods A total of 5239 individuals were randomly sampled and surveyed using the General Anxiety Disorders Scale (GAD-7) to assess the prevalence of anxiety. Boruta found anxiety prevalence predicting factors. We evaluated the decision tree (DT), support vector machines (SVM), random forest algorithm (RF), and extreme gradient boost (XGBoost) using traditional classification (TC) as well as hierarchical classification (HC), and their…