Predicting Student Depression using Machine Learning: A Comparative Analysis of Machine Learning Algorithms for Early Depression Detection in Students
DOI:
https://doi.org/10.63094/AITUSRJ.25.4.1.4Keywords:
Depression Prediction, Machine Learning Models, Logistic Regression, Random Forest, Support Vector Machine , SVM, SMOTE , Synthetic Minority Oversampling Technique , Academic Stress, Financial StressAbstract
Depression among students is emerging as a problem that seriously impairs their academic performance, personal life, and future career prospects. The authors apply machine learning to predict possibilities for depression among students with consideration of a number of personal, academic, and lifestyle variables. Different model types were tried, including logistic regression, random forest, and support vector machine. The performances of all these were checked; among all these, logistic regression yielded the best results with 85% accuracy, and all precision, recall, and F1-score values were also pretty well-balanced. Class imbalance was addressed using SMOTE to improve sensitivity for the model on underrepresented classes. Some of the actionable points to come out of this were focused counseling and support programs by mental health organizations within educational institutions. It also illustrated the use of machine learning, which makes the handling proactive as far as mental health challenges are concerned and opens wider vistas for applications both in the educational and healthcare fields.
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