Mental Health Prediction of Employees at the ...

Mental Health Prediction of Employees at the Workplace Using Machine Learning

Author Name : Dr. G. Arun Kumar, Mr. K H Shabbeer Basha, Sandesh Pokhrel, Jiwan Chaudhary Tharu, Shyam Lal Kafle, Nabin Shahi

ABSTRACT

Mental health is a critical issue in the workplace, affecting the well-being of employees and the productivity of organizations. Machine Learning has the potential to play a significant role in predicting mental health issues and helping organizations to proactively address the needs of their employees. In this work, we propose a machine learning approach to predict mental health issues in the workplace using demographic and job-related input features. The performance of the model is evaluated using metrics such as accuracy, Precision and F1-Score. The result shows that the proposed approach is effective in predicting mental health issues in the workplace and has the potential to be integrated into existing HR systems to provide actionable insights. However, it is important to consider ethical and legal considerations in the use of such models. Future work can focus on incorporating additional factors and developing personalized interventions on the predictions.

Keywords_ Mental Health, Employee, Logistic Regression, K Neighbors Classifier, Decision Tree classifier, Random Forest Classifier, Gradient Boosting Classifier, Ada Boost Classifier, XGB Classifier