Application of machine learning to identify key factors influencing agricultural workers' mental health: A case study of Thai farmers.

Abstract

Objectives: This study examined the associations between pesticide exposures, perceived farm stressors, COVID-19-related stressors, and mental health disorders among Thai farmers. Methods: A total of 270 participants were interviewed to assess mental health disorders. Information was also collected on household environments, agricultural activities, and perceived farm- and COVID-19-related stressors. After data preprocessing, 211 samples remained for analysis. Multiple linear regression models were employed as a baseline, and their performance was compared with ensemble tree-based models, which can capture more complex, nonlinear patterns. The Boruta feature selection technique and SHAP scores were used to explain associations between mental health and the independent variables. Results: Lower levels of mental health disorder symptoms were associated with higher levels of personal protective equipment (PPE) use. Certain perceived farm stressors and COVID-19-related stressors were also correlated with mental health outcomes. Conclusions: The findings indicate that greater PPE use and good agricultural practices are associated with reduced symptoms of mental health disorders. This pilot study highlights the potential of machine learning models to explore complex public health issues involving multiple, interrelated factors.