Document Type
Article
Source of Publication
Scientific Reports
Publication Date
12-1-2026
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.
DOI Link
ISSN
Publisher
Springer Science and Business Media LLC
Volume
16
Issue
1
Disciplines
Business
Keywords
Data balancing techniques, Employee attrition prediction, Feature selection, Hyperparameter optimization, Job change analysis, Machine learning in HR analytics, SHAP explainability
Scopus ID
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Recommended Citation
AL-Ali, Maytha; Alwateer, Majed; Alsaedi, Shatha Abed; Balaha, Hossam Magdy; Badawy, Mahmoud; and A. Elhosseini, Mostafa, "Integrating machine learning and explainable AI for employee attrition prediction in HR analytics" (2026). All Works. 7853.
https://zuscholars.zu.ac.ae/works/7853
Indexed in Scopus
yes
Open Access
yes
Open Access Type
Gold: This publication is openly available in an open access journal/series