Machine Learning Approach to Predict the Mental Health Issues of Employees in Tech Companies

Trailokya Raj Ojha

Abstract

Mental disorders are conditions that have an impact on your emotions, thoughts, and behaviour. It may happen infrequently or have a persistent effect. The topic of mental health has been major and challenging, especially for working professionals. Over time, the urbanized living and workload put a strain on people, making them more vulnerable to mental disorders like anxiety and mental disorders. Working professionals therefore seem to be at an elevated risk of mental health problems. In this study we have analysed data to recognize the characteristics affecting an employee's emotional well-being that can assist in predicting the employee's mental health. A systematic review of machine learning algorithms is done to predict mental health issues in tech industry employees is done. Age, sex, family history of mental health, availability of remote work, and health benefits at work were identified to be main attributes that influence mental health using apriori, random forest, and k-means algorithm. The results indicate that the Random Forest algorithms can predict incidence rates with high scores with an accuracy of 83.3%. With the help of these results, organizations can now focus on finding ways to make their employees less stressful and more pleasant.

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