A Machine Learning-based Framework For Early Mental Health Assessment Among Students

Abstract

Mental Health Concerns Among Students Are Increas- Ingly Prevalent, Yet They Often Go Undetected Due To The Lack Of Structured And Scalable Assessment Frameworks. Conventional Evaluation Methods Tend To Rely On Manual Intervention, Which Is Subjective, Resource-intensive, And Impractical For Continu- Ous Monitoring In Large Educational Settings. The Absence Of Early Detection Mechanisms Can Lead To A Decline In Academic Performance, Social Withdrawal, And Long-term Psychological Consequences. This Study Presents A Machine Learning–based Framework For The Early Identification And Assessment Of Mental Health Risks In Students. The System Leverages Python For Data Processing, Excel For Structured Input Collection, And Supervised Machine Learning Algorithms—particularly Random Forest—for Predic- Tive Modeling. In Addition To Structured Survey Data, The System Integrates Sentiment Analysis Of Open-ended Responses Using TF- IDF Vectorization And Logistic Regression, Allowing For A More Comprehensive Evaluation Of Student Emotional States. Key Features Of The Framework Include Automated Classification Of Mental Health Risk Levels, Real-time Input Monitoring Via File System Event Handling, And Visual Reporting Tools Such As Bar Charts And Confusion Matrices. The Model Achieves An Accuracy Of 93.5% In Classifying Students Into Low, Moderate, Or High Risk Categories, Demonstrating Its Practical Applicability.

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