A Decade of Research on Data Mining Techniques for Predicting Employee Turnover : A Systematic Literature Review

This study presents a comprehensive and systematic review of the data mining (DM) techniques used to predict employee turnover in the past decade. A total of 36 relevant peer-reviewed studies published between 2012 and 2022 were selected. The results indicate that over 20 DM techniques have been used to predict employee turnover in various institutions. In addition, this comprehensive review demonstrates that most machine learning approaches used to predict employee turnover were based on supervised learning, with 94% of the articles (34 out of 36) in this category, particularly random forest followed by deep learning techniques. Furthermore, the review reveals that the most critical factors for predicting employee turnover have been salary and overtime. This study makes a valuable contribution to the field by offering a systematic analysis of the DM algorithms used for predicting employee turnover, in addition to providing an overview of the most significant works in this field produced in the past decade. This study offers important reference regarding the essential DM approaches used in employee turnover prediction and provides future directions for researchers and industries