Predictive Modeling of Mental Health Conditions Using Machine Learning Algorithms

Millions of people of all ages and socioeconomic backgrounds are impacted by mental health illnesses, which are becoming a major global concern. Early detection and intervention are crucial in managing these conditions effectively. In generic diagnostic procedure often rely on subjective assessments and clinical interviews, which may delay diagnosis and treatment, In machine learning (ML), there is increasing potential to invent the predictive procedure that can recognize individuals at cause of mental health issues using behavioral, physiological, and demographic data. A thorough investigation into the use of machine learning methods for the early detection of mental health conditions such bipolar disorder, anxiety, and depression is presented in this research report. A systematic literature review is conducted to evaluate existing models and datasets. To improve prediction accuracy, a unique approach that combines ensemble learning, feature selection, and data preparation is put forth. Real-world datasets sourced from public repositories are used to assess the performance of several machine learning methods, such as Random Forest, Support Vector Machine (SVM), Logistic Regression, and Gradient Boosting. The findings show that ensemble approaches—in particular, XGBoost—perform better, achieving 94.3% accuracy, 92.7% sensitivity, and 95.1% specificity. The results highlight how machine learning might help with early diagnosis and individualized mental health treatment. This study offers a foundation for further research and therapeutic applications while also adding to the expanding corpus of knowledge in computational psychiatry.