Ensemble Learning Approach for Predicting the Necessity of Mental Health Treatments of Employees

The absence of mental health issues defines mental wellness. Rather, mental health is a condition of mental well-being that enables employees to manage work, achieve their potential, learn and work effectively, and have a positive impact on their employment environment. Poor mental health of employers negatively impacts the organization in different ways. It also negatively impacts their cognitive, behavioral, emotional, social, and relational functioning. Therefore, it's essential to recognize the mental health condition without being late and apply suitable treatments. The main objective of this study is to build a model in machine learning to predict the mental health condition and necessity of treatments of employees. The employees from technical, non-technical companies and self-employed were used for this study. The collected data samples are preprocessed and analyzed using the algorithms such as Naïve Bayes, Decision Tree (J48), Support Vector Machine (SVM), Multilayer Perceptron (MLP), Random Forest, and Ensemble Learning. The 10-fold cross-validation was used to validate the results. The J48 was the best single algorithm and its accuracy level was 92.85%. The ensemble learning that combined the above five algorithms had 93.16% accuracy level. When comparing the J48 and ensemble learning, ensemble learning is the best algorithm to forecast the level of necessity of treatments for the mental health of employees.