Assessing University Students' Mental Health through Ensemble Machine Learning: The Role of Academic, Social, and Lifestyle Factors on Depression, Anxiety, and Future Insecurity

Student mental health is becoming an increasingly important problem at academic institutions around the world. The mental health of university students is examined in connection to a number of demographic, academic, and lifestyle variables. Examining the efficacy of students' stress-relieving behaviours was another aspect of the study. Results point to a considerable relationship between students' mental health issues and their lifestyle choices, the amount of work they have to do in school, and their bad experiences on campus.Utilizing algorithms like Random Forest, Decision Tree, XGBoost, and CatBoost, the research focuses on identifying key academic, social, and lifestyle factors that contribute to mental health challenges. The results highlight the predictive accuracy and robustness of ensemble models, offering actionable insights for student mental health interventions.