Authors : Piyush Agarwal; Rahul S Mundaragi; Rahul Sanjay Kohad; Rithvik Allada; Samarth R Bharadwaj; Dr. Shobha T

Volume/Issue : Volume 10 - 2025, Issue 1 - January

Google Scholar : https://tinyurl.com/yc22ntv9

Scribd : https://tinyurl.com/y8ed5mvf

DOI : https://doi.org/10.5281/zenodo.14737958

Abstract : The masses of information surrounding the consequent low academic performance and bad general health of students have brought the issue of depression into the limelight. Both academic stress and personal and social issues act as co- factors in the genesis of depression in students. However, there are a number of challenges with respect to identification of students who are at risk of developing depression owing to the sensitive nature of mental health issues and social stigma. This approach is employing state-of-the-art techniques to predict student depression by analyzing social engagement, academic, and lifestyle-related variables. Three ma- chine learning models were implicated in the study, Logistic Regression, Decision Tree Classifier and Random Forest. The data set consisted of demographic data, self-reported mental health assessments, and academic-related information. The outputs are passed to a single ’Ensemble’ model to improve prediction accuracy. The purpose of this study is to develop a model that is accurate, reliable, and can timely detect student depression and offer useful information to teachers, psychologists, and state policies as a way of helping high- risk students timely. Hence, it aims towards a more positive academic environment.

Keywords : Depression, Machine Learning, Ensemble Model, Academic Stress, Mental Health.

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