Machine Learning-Based Predictive Framework for Early Identification of Mental Health Disorders
Authors-Pitla Shravani, K.Rajkumar
Keyword-Mental Health Prediction, Machine Learning, Supervised Learning, Logistic Regression, Sup-port Vector Machine, Psychological Disorder Detection, Healthcare Analytics, Questionnaire-Based Screening.
Mental health disorders have become a significant public health concern due to increasing aca-demic, professional, and social pressures experienced by individuals worldwide. Early identifi-cation of psychological conditions can substantially improve treatment outcomes and reduce long-term complications. This research presents an intelligent machine learning framework for automated mental health assessment using structured self-report questionnaires. The proposed system utilizes two questionnaire modules to identify general mental health conditions and clas-sify five common psychological disorders, namely Bipolar Disorder, Anxiety Disorder, Depres-sion, Eating Disorder, and Sleep Disorder. Supervised machine learning algorithms including Logistic Regression, Decision Tree, Support Vector Machine (Linear and RBF kernels), and Naïve Bayes are employed to analyze questionnaire responses and generate predictive outcomes. Logistic Regression is adopted for initial mental health screening, while Support Vector Machine with a linear kernel demonstrates superior performance for multiclass disorder identification. The experimental evaluation conducted on 1,253 valid questionnaire responses confirms the effec-tiveness of the proposed framework, achieving high classification accuracy while maintaining a simple and cost-effective implementation. The developed model can serve as an efficient prelimi-nary screening tool to support healthcare professionals, educational institutions, and individuals in recognizing potential mental health risks at an early stage.
Doi-[https://doi.org/10.5281/zenodo.21735677]