Machine Learning with Human Resources Data: Predicting Turnover among Community Mental Health Center Employees

Background: Human resources (HR) departments collect extensive employee data that can be useful for predicting turnover. Yet, these data are not often used to address turnover due to the complex nature of recorded data forms. Aims of the Study: The goal of the current study was to predict community mental health center employees’ turnover by applying machine learning (ML) methods to HR data and to evaluate the feasibility of the ML approaches. Methods: Historical HR data were obtained from two community mental health centers, and ML approaches with random forest and lasso regression as training models were applied. Results: The results suggested a good level of predictive accuracy for turnover, particularly with the random forest model (e.g., Area Under the Curve was above .8) compared to the lasso regression model overall. The study also found that the ML methods could identify several important predictors (e.g., past work years, wage, work hours, age, job position, training hours, and marital status) for turnover using historical HR data. The HR data extraction processes for ML applications were also evaluated as feasible. Discussion: The current study confirmed the feasibility of ML approaches for predicting individual employees’ turnover probabilities by using HR data the organizations had already collected in their routine organizational management practice. The developed approaches can be used to identify employees who are at high risk for turnover.