Unleashing the Power of Machine Learning: Revolutionizing Early Diagnosis and Prevention of Mental Health Disorder

In modern times, mental health issues have become an important concern with far-reaching effects on both individuals and companies. This research paper investigates the impact of geographical location, work environment, gender, and employment status on mental health illness prediction. This study aims to investigate the possible connections between these variables and mental health outcomes, as well as individuals' attitudes towards mental health. This study seeks to identify the strongest factors influencing mental health disorders and attitudes, contributing to our knowledge of the complex mechanisms behind mental well-being. To achieve the objective, various classification models were used, including the Random Forest Classifier, AdaBoost Classifier, Gradient Boost Classifier, XGB Classifier, and a hybrid Stacking Model. The models were trained and evaluated using various metrics to measure their performance. The metrics considered were train and test accuracy, precision, recall, F1- score, ROC curve, and AVC. The metrics mentioned are important indicators of the models' predictive accuracy, ability to distinguish, and overall performance. In this study, we conducted a comparative analysis of various techniques and successfully implemented them. Through our evaluation, we identified the Stacking technique as the most accurate method, achieving a prediction accuracy of 82.27%. This study aims to contribute towards addressing the potential rise of a widespread “mental health epidemic.”