Abstract
Employee attrition poses significant challenges to organizations, impacting productivity, morale, and financial stability. Predicting attrition and understanding its underlying drivers are critical for implementing effective retention strategies. In this study, we propose a comprehensive framework that utilizes advanced machine learning techniques to predict employee attrition and job change likelihood. The framework integrates robust preprocessing pipelines, state-of-the-art predictive models, and explainability tools such as SHAP (SHapley Additive exPlanations) to ensure transparency and fairness in HR analytics. By addressing key challenges such as class imbalance, feature selection, and model interpretability, our approach provides actionable insights for proactive talent management. We evaluate the framework on multiple datasets (including the IBM HR Analytics Employee Attrition & Performance dataset and the HR Analytics: Job Change of Data Scientists dataset), achieving near-optimal performance metrics across diverse scenarios. Notably, the Adaptive Boosting (AB) and Histogram Gradient Boosting (HGB) models demonstrate superior performance, with high Precision, Recall, F1-score, and Accuracy. Global and local interpretability analyses using SHAP visualizations reveal critical predictors of attrition, such as OverTime, JobLevel, and JobSatisfaction, enabling targeted interventions. The results underscore the framework’s adaptability, scalability, and potential for real-time deployment in organizational settings. This study contributes to advancing HR analytics by bridging gaps in predictive accuracy, interpretability, and generalizability; offering practical solutions for mitigating employee turnover and safeguarding human capital investments.
Data availability
The current study utilized four datasets: 1. The IBM HR Analytics Employee Attrition & Performance dataset available at: https://www.kaggle.com/datasets/pavansubhasht/ibm-hr-analytics-attrition-dataset 2. The “HR Analytics: Job Change of Data Scientists” dataset available at: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists 3. The HR Dataset v14 is a synthetic dataset available at: https://rpubs.com/rhuebner/hrd_cb_v14. 4. The Attrition Rate of a Company dataset available at: https://www.kaggle.com/datasets/anujachintyabiswas/attrition-rate-of-a-company
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Acknowledgements
During the preparation of this manuscript, the authors used ChatGPT (OpenAI, GPT-5, 2025 version) for text refinement, including grammar, sentence structure, and clarity improvements. The authors reviewed and edited the output and take full responsibility for the content of this publication. No part of the research design, data analysis, results interpretation, or scientific conclusions relied on AI-generated content.
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Conceptualization, Maytha AL-Ali, Majed Alwateer, and Shatha Abed Alsaedi; Data curation, Shatha Abed Alsaedi and Hossam Magdy Balaha; Formal analysis, Maytha AL-Ali, Mahmoud Badawy, and Mostafa A. Elhosseini; Investigation, Majed Alwateer, Shatha Abed Alsaedi and Hossam Magdy Balaha; Methodology, Shatha Abed Alsaedi, Hossam Magdy Balaha and Mahmoud Badawy; Software, Shatha Abed Alsaedi and Hossam Magdy Balaha; Supervision, Mahmoud Badawy and Mostafa A. Elhosseini; Validation, Maytha AL-Ali and Majed Alwateer; Visualization, Shatha Abed Alsaedi and Hossam Magdy Balaha; Writing - original draft, Maytha AL-Ali, Majed Alwateer, Shatha Abed Alsaedi, and Hossam Magdy Balaha; Writing - review & editing, Mahmoud Badawy and Mostafa A. Elhosseini.
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AL-Ali, M., Alwateer, M., Alsaedi, S.A. et al. Integrating machine learning and explainable AI for employee attrition prediction in HR analytics. Sci Rep (2026). https://doi.org/10.1038/s41598-026-36424-2
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DOI: https://doi.org/10.1038/s41598-026-36424-2
