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IITM Journal of Information Technology

ISSN (P) 2395-5457 | Single Blind Peer Reviewed Journal

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INSTITUTE OF INNOVATION IN TECHNOLOGY & MANAGEMENT
Affiliated to GGSIPU, NAAC Grade ‘A’, ISO 14001:2015, 17020:2012, 21001:2018 & 50001:2018 Certified,
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Comparative Evaluation of Machine Learning Algorithms for Early Prediction of Student Mental Health Risk

Tushita1, Aashima2, Manpreet Singh3
1, 2Research Scholar, 3Assistant Professor,1, 2, 3Maharaja Surajmal Institute (GGSIPU), New Delhi, India

Abstract: In recent years, psychological distress has become a serious concern worldwide. Academic competition, financial problems, unhealthy lifestyle patterns, and societal expectations together contribute to increased levels of stress and anxiety among students. Due to these continuous pressures, students may develop mental health problems that negatively affect their well-being and academic performance. Therefore, early identification of vulnerable students is essential in order to provide timely intervention and preventive support. This research presents a systematic machine learning–based framework developed using survey data containing demographic and behavioral attributes, which are used to predict mental health risk. The main objective of this study is to identify patterns that indicate psychological vulnerability. In this research, four techniques are applied: Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest. These techniques are implemented to classify students into high- risk and low-risk groups. Before the development of the model, several operations were performed on the dataset, including preprocessing steps such as treating missing values and encoding features. After that, the numerical variables were normalized and the dataset was divided into training and testing subsets in order to achieve robustness and better generalization capability of the model. Furthermore, the model was evaluated using commonly used performance metrics such as accuracy, precision, recall, F1-score, and confusion matrix. From the experimental results, it was observed that the Random Forest algorithm produced the most accurate classification and balanced metric performance compared with the other models. In the later phase of the project, an ensemble majority voting strategy, improved statistical validation, and a structured evaluation framework were also included to improve the stability of the predictions. Therefore, the proposed system provides an evidence based approach that can help educational institutions proactively identify students at risk of mental health problems and provide timely support and intervention.

Keywords: Student Mental Health, Machine Learning, Random Forest, SVM, Predictive Analytics, Risk Classification

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