A Machine Learning-Based Predictive Model of Return to Work After Sick Leave

This is the authors' abstract. We don't add key points for this paper.

OBJECTIVE: This study aims to build a predictive model for "return to work" (RTW) after sick leave by using a machine-learning algorithm. METHODS: Panel data of 2000 participants (1686 males and 314 females) from the Labor Welfare Research Institute of the Korea Workers' Compensation & Welfare Service were used. A gradient boosting machine (GBM) was used to build the predictive model. RESULTS: The GBM showed excellent performance in a binary classification (returned to work vs not working). However, the model of the three-group classification showed suboptimal performance. CONCLUSIONS: Although machine-learning algorithms using common predictive factors can accurately predict whether one can work after sick leave, they cannot differentiate the form of returning to work. Future research with detailed information based on the injury or disease is warranted.