Stress and Mental Health Prediction Using Machine Learning

Mentalhealthisanimportantpartofemployeewell-beingandorganizationalefficiency,especiallyinthetechnologyindustry where job stress is high. Predictive modeling would be able to recognize persons at risk of pursuing mental health treatment to enable early intervention and resource allocation. In this research, we used machine learning methods on a workplace mental health survey database to predict the respondents' treatment-seeking behavior. Preprocessing involved dealing with missing values, normalization of categorical responses like gender, outlier correction of ages, and scaling of numerical features. Class imbalance was tackled using the Synthetic Minority Oversampling Technique (SMOTE). Three algorithms were used and compared: Random Forest, K-Nearest Neighbors (KNN), and AdaBoost. The outcome showed that Random Forest had the best accuracy at 83.33%, with slightly lower but comparable performances by KNN and AdaBoost. These results underscore the appropriateness of instance-based and ensemble approaches to mental health prediction tasks and infer that further enhancements such as hyper parameter optimization, sophisticated encoding techniques, and model ensemble can further lead to higher predictive rates above 88%