MindTrack: Predicting Student Mental Health Risk Using Machine Learning

Abstract

Student mental health has a direct bearing on academic performance and overall well-being, yet problems such as stress, anxiety and depression usually go unnoticed until they have already affected a student's grades or attendance. Counsellors and academic staff typically rely on manual observation, self-reported questionnaires, or referrals to identify students who may be struggling, an approach that is reactive rather than preventive and does not scale well across large student populations. This paper presents MindTrack, a machine learning based framework for predicting student mental health risk from a combination of academic and psychological indicators, namely age, gender, study hours, sleep hours, attendance, CGPA, stress level, anxiety level and depression level. Five supervised classifiers — Support Vector Machine (SVM), Random Forest, Decision Tree, K-Nearest Neighbors (KNN) and Naive Bayes — were trained on a dataset of 12,000 student records and were independently validated on a separate set of 399 records so that the reported performance reflects behaviour on unseen data rather than the training data itself. Among the five models, the Support Vector Machine produced the most balanced and reliable results, reaching a validation accuracy of 80.45% and an F1-score of 0.8203. A Random Forest based feature importance analysis further showed that stress level, anxiety level and depression level were the strongest predictors of risk, together accounting for more than 71% of the total feature contribution. All five trained models were deployed inside a Flask based web dashboard that walks a user through an Overview, Academic Info, Health Indicators and Results screen, returning an instant High Risk / Low Risk classification along with a side-by-side comparison of every model and a feature importance chart. The results indicate that combining routinely available academic records with short psychological self-assessments can support earlier and more consistent identification of at-risk students than manual counselling alone.