Ai-based Early Detection Of Mental Stress Among College Students Using Machine Learning Techniques

Abstract

Mental Stress Among College Students Has Become A Significant Concern Due To Academic Pressure, Social Expectations, And Lifestyle Changes. Early Identification Of Stress Levels Can Help Institutions Provide Timely Psychological Support And Prevent Serious Mental Health Issues. This Paper Proposes An Artificial Intelligence-based Predictive Model For Early Detection Of Mental Stress Among College Students Using Machine Learning Techniques. Data Such As Academic Performance, Attendance, Sleep Patterns, Social Activity, And Self-reported Stress Indicators Are Analysed. Machine Learning Algorithms Including Logistic Regression, Decision Tree, Random Forest, And Support Vector Machine Are Implemented And Evaluated. Experimental Results Indicate That The Random Forest Classifier Achieves Superior Performance In Stress Prediction. The Proposed System Can Assist Educational Institutions In Implementing Preventive Mental Health Strategies.

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