Prediction of Health Care Employee Turnover using Gradient Boosting Algorithm

Abstract

Employee turnover is a measurement of how many employees are leaving a company and how many employees are retaining. Retaining the Employee is going to be biggest challenge for any of the organization in the world. Small to l arge organizations are hugely affected by such a big employee turnover problem. The healthcare industries are hugely affected by employee turnover problem. When employee leave the organization, the organization must have to replace such a employee with the employee having same skills, experience, behavior etc. This employee turnover prediction model uses the machine learning gradient boosting algorithms namely XGBoost, CatBoost, and LightGBM algorithms. Each models are trained, tested and validated and checked the performance metrices based on Pearson’s correlation coefficients. Employee turnover prediction model helps to the supervisor to have a frequent, timely and relevant interactions or actions with the employee group based on prediction. Based on the prediction the supervisor may take actions on the employee before termination.