Advancing Mental Health Problems with Machine Learning and Genetic Algorithms for Anxiety Classification in Bangladeshi University Students

Shahriar Siddique Ayon, Muhammad Ebrahim Hossain, Md. Saef Ullah Miah, M. Mostafizur Rahman, Mufti Mahmud

Abstract: Mental health challenges, particularly anxiety, are a growing concern among university students in Bangladesh, impacting both their well-being and academic performance. This study aims to address this growing issue by developing a robust machine learning model tailored to classify anxiety levels among students. We employed eight well-known machine learning algorithms, with particular emphasis on a customized Genetic Algorithm (GA) optimized Logistic Regression (LR) model. The models were rigorously trained and evaluated using 5-fold cross-validation on a newly obtained mental health dataset that had never been investigated with such advanced approaches. This dataset contains information on 1,977 students from 15 of Bangladesh’s leading universities and was thoroughly examined by five academics with 10 to 20 years of experience in academia and research. Our findings indicate that classic models, such as the Random Forest Classifier and Support Vector Classifier, attained accuracies of 92.68% and 96.72%, respectively. However, our proposed GA-based LR model succeeded them all, not only in accuracy but also in precision, recall, and F1-score, with an impressive 99.49% accuracy. The study also identified key anxiety-inducing factors, such as academic pressure and worry about academic affairs, providing valuable insights for targeted mental health interventions. These findings indicate the effectiveness of our customized GA-based ML model in enhancing mental health evaluations by identifying the fundamental causes of anxiety, providing vital insights to help Bangladeshi university students maintain their mental health.