Machine Learning Approaches to Workplace Mental Health: Predicting Treatment-Seeking Behavior Using the OSMI Dataset
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Abstract
Employee mental health is increasingly recognized as essential for sustainable organizational performance, particularly in technology sectors where work intensity and psychological strain are prevalent. This study leverages machine learning to identify predictors of treatment-seeking behavior using the Open Sourcing Mental Illness (OSMI) dataset, which includes 1,387 anonymized responses from the 2014 OSMI survey. The survey examines employees’ experiences and perceptions of mental health in the global tech industry. Through data cleaning and encoding, key factors influencing help-seeking behavior were identified, including family history of mental illness and work interference due to psychological distress. Two machine learning models, Decision Tree and K-Nearest Neighbour (KNN), were employed for prediction. The Decision Tree model achieved an accuracy of 73%, while KNN attained 100%, suggesting high predictive power, albeit with potential overfitting risks. These findings align with recent studies promoting the integration of AI-driven analytics in workplace wellness programs to detect hidden behavioral trends and enable early interventions. The results demonstrate that machine learning models can offer valuable insights into employee well-being and preventative strategies. Future research should focus on incorporating larger, more diverse datasets and adopting explainable AI (XAI) techniques to enhance interpretability, fairness, and trust in predictive systems for mental health in the workplace.
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