Machine Learning-Based Forecasting of Mental Health Issues Among Employees in the Workplace

The management of mental health issues in the workplace has always been a significant and challenging task, especially for professionals. Despite the evidence of the detrimental effects of preventable mental health disorders and stress in the workplace, many organizations have not taken enough preventative measures. To address this issue, data were collected from the OSMI website, which aims to raise awareness of mental illness in the workplace. The collected data was label encoded to improve prediction accuracy. Various machine learning techniques were applied to the data to develop a model to help individuals with mental health issues create a healthier work environment. Our proposed approach involved the implementation of classification algorithms, including Random Forest, Logistic Regression, Support Vector Machine, Adaboost, and Gradient Boosting, to obtain the highest accuracy possible.