Mental Health Prediction Among Medical Students Using Machine Learning

Abstract

Mental health is a critical aspect of well-being, influencing individuals' ability to function in daily life and cope with stress. Medical students struggle with depression alongside their demanding academic schedule and long workdays since these circumstances harm their academic results and general health condition. The objective of this research considers the application of machine learning algorithms to detect depression severity levels in medical students. Data contains a total of fourteen features with demographic information from age and year of study and psychological indicators such as interest scores compared to pleasure levels in addition to ratings regarding fatigue and sleep problems as well as changes in appetite and concentration abilities and PHQ-9 scoring scales. The depression severity makes up the target variable which divides into four groups: Severe Depression, Moderate Depression, Mild Depression and Moderately Severe Depression. The assessment of this task incorporated numerous machine learning models which consisted of Gaussian Naive Bayes, Random Forest Classifier, AdaBoost Classifier, Logistic Regression and Support Vector Classifier (SVC). Random Forest Classifier delivered the highest accuracy of 99.04% while Logistic Regression reached an accuracy of 98.80% yet Gaussian Naive Bayes obtained 97.36% accuracy. The accuracy of Support Vector Classifier at 95.44% was lower than the other models which included AdaBoost and its weakest performance of 79.41%. Predictions of depression severity among medical students demonstrate optimal performance when using Random Forest as the model selection because of its strong predictive capabilities. The study reveals crucial roles which machine learning serves mental health prediction alongside giving researchers a way to recognize at-risk students for proper early interventions and individualized treatments. Additional optimization and more targeted feature engineering techniques seem necessary to raise AdaBoost's results since there exists room for increased model accuracy. Researchers show that artificial intelligence performs effectively for mental health detection because it helps identify different depression severity levels within medical students who face elevated stress and health challenges