Employee Churn Prediction in Healthcare Industry using Supervised Machine Learning

Sammanfattning: Given that employees are one of the most valuable assets of any organization, losing an employee has a detrimental impact on several aspects of business activities. Loss of competence, deteriorated productivity and increased hiring costs are just a small fraction of the consequences associated with high employee churn. To deal with this issue, organizations within many industries rely on machine learning and predictive analytics to model, predict and understand the cause of employee churn so that appropriate proactive retention strategies can be applied. However, up to this date, the problem of excessive churn prevalent in the healthcare industry has not been addressed. To fill this research gap, this study investigates the applicability of a machine learning-based employee churn prediction model for a Swedish healthcare organization. We start by extracting relevant features from real employee data followed by a comprehensive feature analysis using Recursive Feature Elimination (RFE) method. A wide range of prediction models including traditional classifiers, such as Random Forest, Support Vector Machine and Logistic Regression are then implemented. In addition, we explore the performance of ensemble machine learning model, XGBoost and neural networks, specifically Artificial Neural Network (ANN). The results of this study show superiority of an SVM model with a recall of 94.8% and a ROC-AUC accuracy of 91.1%. Additionally, to understand and identify the main churn contributors, model-agnostic interpretability methods are examined and applied on top of the predictions. The analysis has shown that wellness contribution, employment rate and number of vacations days as well as number of sick day are strong indicators of churn among healthcare employees.