Predicting Mental Health Treatment Seeking in the Technology Industry Using Machine Learning: A Comparative Analysis of Supervised Classification Models

Background Mental health disorders affect approximately one in four people globally, yet treatment-seeking rates remain persistently low, particularly in high-stress professional environments such as the technology industry. Understanding the factors that predict whether an individual will seek mental health treatment is critical for designing effective workplace interventions. Methods This study applies five supervised machine learning classification algorithms — Random Forest, Logistic Regression, Support Vector Machine (SVM), Gaussian Naive Bayes, and eXtreme Gradient Boosting (XGBoost) — to predict treatment-seeking behaviour using the 2016 Open Sourcing Mental Illness (OSMI) survey dataset. The dataset includes responses from 1,434 technology industry workers across multiple countries. After preprocessing, including removal of high-missingness features and standardisation of categorical fields, a refined dataset of 960 entries was used for model training and evaluation. Feature correlation analysis was conducted to identify the strongest predictors of treatment-seeking behaviour. Results XGBoost achieved the highest classification accuracy of 88.7%, outperforming Random Forest (87.1%), Logistic Regression (87.1%), Gaussian Naive Bayes (86.6%), and SVM (85.6%). The most significant predictors of treatment-seeking behaviour were a prior diagnosis of a mental disorder and a family history of mental illness. A marked gender disparity was observed: male-identifying respondents reported substantially lower treatment-seeking rates despite similar rates of self-reported mental disorders. Conclusions Machine learning approaches, particularly XGBoost, demonstrate strong predictive capability for mental health treatment-seeking behaviour in technology industry workers. The identified gender disparity suggests a need for targeted workplace mental health interventions directed at male-identifying employees. These findings contribute to the growing evidence base for data-driven approaches to mental health decision support.