Abstract
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 performance was evaluated using parameters of Confusion matrix, ROC curves, and the cross-validation.
Results
Among the respondents, one-third of them reported a severe level of anxiety. Participants' family problems, drug addiction, and eleven more were selected as risk factors predicting anxiety by using Boruta. The performance was tested based on two different classification techniques, considered traditional classification and hierarchical classification. Overall, the hierarchical classification in terms of local classification of the xtreme gradient boosting model (Accuracy = 0.926, Sensitivity = 0.987, Specificity = 0.22, F-score = 0.963, and AUC = 0.71) performed better and authentically predicted anxiety.
Conclusion
The findings offer legislators, stakeholders, and household members an opportunity to address this significant crisis proactively through enhanced policy, concentrated psychological well-being promotion, and the development of extremely effective mental health services.