Mental Health Status Prediction Using Machine Learning

The increasing prevalence of mental health challenges among students and young adults has become a major concern, posing significant risks to academic performance, emotional well-being, and overall quality of life.While existing studies often focus on self-reported assessments and clinical evaluations, this study argues that lifestyle and behavioral patterns provide more consistent and scalable indicators of mental stress than isolated assessments alone.This research proposes a mental health stress prediction framework using Machine Learning techniques and evaluates the performance of supervised learning models such as Logistic Regression, Support Vector Machine, and Random Forest.Furthermore, the study incorporates feature-level interpretability by analyzing the influence of lifestyle attributes through model-driven feature contributions to identify the factors most strongly associated with elevated stress levels.By shifting the analytical focus from manual evaluation methods to automated predictive analysis, this research aims to provide scalable and efficient tools for early mental health risk identification.The findings are expected to demonstrate that insufficient sleep, excessive screen usage, emotional instability, and prior mental health history are stronger predictors of stress than demographic factors alone, thereby contributing to more reliable and interpretable mental health prediction systems.