Enhanced Detection of Student Depression Using an Optimized Machine Learning Model

Abstract

Depression is an increasing concern among students adversely affecting academic performance and mental well-being. Early prediction is important for timely intervention. This paper aims to classify students into "Depressed" or "Not Depressed" categories utilizing the Depression Student Dataset. A comparative analysis of several traditional machine learning (ML) approaches, counting Gradient Boosting (GB), Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), XGBoost, LightGBM, AdaBoost (AB), Naïve Bayes (NB), and Decision Tree (DT) was performed to evaluate their predictive abilities. To enhance accuracy of prediction, this paper proposed an optimized LR model fine-tuned utilizing GridSearchCV. The optimized model shows superior performance with an accuracy rate of 98%, outstanding all other algorithms in this study. The findings highlight the efficiency of model tuning in improving depression classification results. This research proposed a robust framework for used ML to classify depression among students, contributing to early pridiction and support strategies.