Artificial Intelligence-Based Approach for Classification and Prediction of Mental Health

Mental health evaluation is a crucial component in early identification and treatment of mental illnesses. Mental health disorders should be classified and processed under predictive analysis for their timely and effective treatments. This study attempts to design an Artificial-Intelligence based approach to bridge the gap between clinical expertise and technological advancements. This can be accomplished by visualizing large datasets, and applying different types of machine learning techniques. This not only enhances diagnostic precision but also helps predict mental health problems. The goal is to perform comparative analysis on different machine learning algorithms and classify mental health issues. The study underlines the multiple factors that contribute to the mental health disorders guiding clinicians and professionals in decision making. By utilising various supervised machine learning models, XGBoost model achieves an astounding accuracy of 99.85% which shows to be the most effective one for detecting mental health followed by Decision Tree with 99.76%. XGBoost is robust and achieves high accuracy due to its ensemble learning technique on decision trees.