Machine Learning Approaches for Predicting Employee Turnover: A Systematic Review

Abstract

Employee turnover prediction remains a critical issue for organizations aiming to improve talent retention and minimize recruitment costs. The ability to predict when and why employees are likely to leave enables companies to take proactive measures to reduce turnover rates. This paper presents a systematic review of 58 studies focused on applying machine learning (ML) algorithms to predict employee turnover. We analyze various ML techniques, including Random Forest, Support Vector Machine, Logistic Regression, Decision Tree, and Neural Networks, highlighting their effectiveness in predicting turnover based on employee data. The review reveals that Random Forest emerged as the most widely used technique, achieving high predictive accuracy across multiple studies. Among the features, Job Satisfaction was identified as the most critical factor in turnover prediction, appearing in a majority of studies. Additionally, large datasets (more than 10,000 samples) were predominantly used, suggesting that more comprehensive data improve model performance. This review emphasizes the potential of ML in HR analytics and provides valuable insights into the strengths and limitations of each method.