Proceedings of the International Conference on Intelligent Systems and Digital Transformation (ICISD 2025)

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Analyzing Workforce Attrition Through Advanced Analytics

Authors
Jansi Rani1, Kiruthiga Ramaswami1, Aditi Walunj1, T. Anusha1, *
1Department of Computer Science and Engineering, SRM Institute of Science and Technology - Vadapalani Campus, No. 1 Jawaharlal Nehru Road, Vadapalani, Chennai, TN, India
*Corresponding author. Email: anushat@srmist.edu.in
Corresponding Author
T. Anusha
Available Online 31 October 2025.
DOI
10.2991/978-94-6463-866-0_47How to use a DOI?
Keywords
Employee Attrition Prediction; HR Analytics; Supervised Learning; XGBoost; LightGBM; CatBoost; AdaBoost; Feature Engineering; Hyperparameter Tuning; Grid Search; SMOTE; Support Vector Machine
Abstract

Analyzing employee attrition helps us understand core issues employees may be facing, like challenging workloads, inadequate compensation and psychological distress. Our project tries to understand reasons for attrition in healthcare—an industry where it is critical that the employees function efficiently, ensuring that patient care is not compromised. Our model combines the strengths of multiple boosting algorithms to increase the accuracy and to reduce the bias and variance, while also helping us improve the prediction on unseen data. Unlike existing approaches, our model combines the three models in a stacking ensemble, ensuring a more robust result. We aim to tackle class imbalance with SMOTE, improving generalization and recall. The desired result would be for our model to be a reliable tool for hospitals and clinics to better understand the issues that their staff face while also predicting what percentage of employees may quit the organization.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Intelligent Systems and Digital Transformation (ICISD 2025)
Series
Atlantis Highlights in Intelligent Systems
Publication Date
31 October 2025
ISBN
978-94-6463-866-0
ISSN
2589-4919
DOI
10.2991/978-94-6463-866-0_47How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Jansi Rani
AU  - Kiruthiga Ramaswami
AU  - Aditi Walunj
AU  - T. Anusha
PY  - 2025
DA  - 2025/10/31
TI  - Analyzing Workforce Attrition Through Advanced Analytics
BT  - Proceedings of the International Conference on Intelligent Systems and Digital Transformation (ICISD 2025)
PB  - Atlantis Press
SP  - 564
EP  - 575
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-866-0_47
DO  - 10.2991/978-94-6463-866-0_47
ID  - Rani2025
ER  -