Employee Attrition Prediction Using Deep Neural Networks

Dhafer G. Honi · Francesca Fallucchi · Ayad Ibrahim Abdulsada · Romeo Giuliano · Husam A. Abdulmalik

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AI and HR Technologies Online Learning and Analytics Customer churn and segmentation

Abstract

Decision-making plays an essential role in the management and may represent the most important component in the planning process. Employee attrition is considered a well-known problem that needs the right decisions from the administration to preserve high qualified employees. Interestingly, artificial intelligence is utilized extensively as an efficient tool for predicting such a problem. The proposed work utilizes the deep learning technique along with some preprocessing steps to improve the prediction of employee attrition. Several factors lead to employee attrition. Such factors are analyzed to reveal their intercorrelation and to demonstrate the dominant ones. Our work was tested using the imbalanced dataset of IBM analytics, which contains 35 features for 1470 employees. To get realistic results, we derived a balanced version from the original one. Finally, cross-validation is implemented to evaluate our work precisely. Extensive experiments have been conducted to show the practical value of our work. The prediction accuracy using the original dataset is about 91%, whereas it is about 94% using a synthetic dataset.

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References

2008The Happy-Productive Worker Thesis Revisited 2020Predicting Employee Attrition Using Machine Learning Techniques 2018Artificial Intelligence, Economics, and Industrial Organization 2018Predicting Employee Attrition using Machine Learning 2021An Improved Machine Learning-Based Employees Attrition Prediction Framework with Emphasis on Feature Selection

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2023A decade of research on machine learning techniques for predicting employee turnover: A systematic literature review 2024Enhancing the Prediction of Employee Turnover With Knowledge Graphs and Explainable AI 2025Predicting Employee Attrition: XAI-Powered Models for Managerial Decision-Making 2023An Enriched Employee Retention Analysis System with a Combination Strategy of Feature Selection and Machine Learning Techniques 2025Employee Retention Analytics: Predictive Modeling for Workforce Stability and Talent Management 2025A Deep Learning Model Based on Bidirectional Temporal Convolutional Network (Bi-TCN) for Predicting Employee Attrition 2023AI in HRM: case study analysis. Preliminary research

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