Predictionof Mental HealthUsing Machine Learning

Abstract

Mental health disorders have emerged as a paramount problem worldwide, affecting millions and presenting substantial hurdles to healthcare systems. In this research, a method for predicting mental health disorders utilizing extensive machine learning (ML) and artificial intelligence (AI) models is proposed. The proposed system integrates a range of sophisticated machine learning algorithms to analyze user inputs and predict potential mental health issues. By selecting the feature importance, we can select the best suitability model for prediction with high accuracy. The algorithms, K-Neighbors Classifier, Decision Tree Classifier, Random Forest, Boosting and Stacking, are used to predict mental health. Among them, Boosting appears to be the best model based on its highest F1 score. The anticipated likelihood condition was evaluated to make an appropriate recommendation. We focus on college students and older adults, specifically adults older than 18 years. This demographic is at higher risk of mental health challenges due to academic and workplace stress, making early intervention crucial.