Mental Health Analytics: A Comparative Exploration of Machine Learning Models

Machine learning methods for checking mental health: findings, understanding, and how well the models work. In short, this study explored how machine learning tools can be used to assess mental health by analyzing a large amount of information about people, including their backgrounds, lifestyles, and social behaviors. Using a Boosting model, a Decision Tree Classifier, and a logistic regression model helped us thoroughly evaluate how well the models performed. The Boosting model worked the best, reaching an accuracy of 81.75% Next came the Decision Tree Classifier with an accuracy of 80.69%, and the logistic regression model had an accuracy of 79.63% Besides checking how well the models work, the research also looked at ways to improve the models so they can be better understood. It focused on finding the important factors that greatly affect mental health results. We used techniques called hyperparameter tuning and cross-validation to make sure the models are strong and can work well on new data. The suggested model could help find people who are likely to develop different mental health issues. Feature importance analysis can give useful guidance to doctors, and future treatment plans in behavioral medicine can be created based on these predictions. AI models can help show the link between life, lifestyle choices, and high stress levels. This paper aims to help improve fast AI learning for mental health evaluation and highlight concerns with using data-related methods to assess social and health problems.