Development of a Student Depression Prediction Model Based on Machine Learning with Algorithm Performance Evaluation
Abstract
This research explores the implementation of machine learning to predict depression among university students using a dataset of 2.028 responses containing PHQ-9 scores and academic-demographic attributes. The research implements a structured modeling process involving feature selection, normalization, the model’s efficacy was gauged through a suite of evaluate measures, encompassing accuracy, precision, recall, F1-score, The support vector machine (SVM) model’s accuracy improved from 58.8% to 99.5% after hyperparameter tuning. This investigation lends itself to the advancement of a proactive identification framework, which hold potential for incorporation within collegiate mental well-being surveillance infrastructures. Future implementations may consider real-time models and expand data sources through digital counseling systems and behavioral analytics
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