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Original Article
Developing a Transparent Anemia Prediction Model Empowered with Explainable AI
Dr. B. Lakshma Reddy1
Ambika H2
Chandrika V3
Divyashree C S4
1 Professor, Department of Computer Science and Engineering, Rajarajeswari College of Engineering, Bangalore, Karnataka, India. 2 3 4 Department of Computer Science and Engineering, Rajarajeswari College of Engineering, Bangalore, Karnataka, India.
Published Online: September-December 2025
Pages: 301-304
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20250403047
References
1. S. Jain, M. K. Patel, and R. S. Meena, “Machine learning based diagnosis of anaemia using clinical data,” International Journal of Medical Informatics, vol. 145, p. 104299, 2020, Doi: 10.1016/j.ijmedinf.2020.104299.
2. Yadav and S. Singh, “Anaemia detection using machine learning algorithms,” International Journal of Computer Applications, vol. 180, no. 40, pp. 10–15, 2018, Doi: 10.5120/ijca2018917286.
3. A. Kumar and P. Choudhury, “Predictive analytics for anaemia detection using decision trees and random forest,” Journal of Biomedical Engineering and Medical Imaging, vol. 7, no. 6, pp. 45–52, 2020, Doi: 10.14738/jbemi.76.8526.
4. S. Ahmed, M. R. Islam, and M. A. Rahman, “Using machine learning to detect anaemia: A comparative study,” Health Informatics Journal, vol. 27, no. 1, pp. 1–14, 2021, Doi: 10.1177/1460458220975594.
5. T. S. Dey and S. Mondal, “Explainable AI for anaemia classification using SHAP values,” Journal of Medical Systems, vol. 44, no. 12, p. 223, 2020, Doi: 10.1007/s10916-020-01667-5.
6. V. K. Sharma and M. Joshi, “Comparative analysis of ML algorithms for anaemia prediction,” Procedia Computer Science, vol. 167, pp. 2180–2189, 2020, Doi: 10.1016/j.procs.2020.03.269.
7. A. Gupta, R. Das, and H. Roy, “An intelligent system for anaemia prediction using ensemble learning,” International Journal of Intelligent Engineering and Systems, vol. 14, no. 5, pp. 220–230, 2021, Doi: 10.22266/ijies2021.1031.20.
8. M. Farid and A. Nasreen, “XGBoost and stacking models for improving anaemia classification accuracy,” International Journal of Healthcare Information Systems and Informatics, vol. 16, no. 1, pp. 1–24, 2021, Doi: 10.4018/IJHISI.2021010102.
9. P. Kumar and R. Sharma, “SHAP and LIME-based explainability in medical AI: Case study on anaemia,” Artificial Intelligence in Medicine, vol. 107, p. 101909, 2020, Doi: 10.1016/j.artmed.2020.101909.
10. M. A. Hossain, S. N. Nahar, and F. Rahman, “Prediction of anaemia in women using machine learning techniques,” in Proc. Int. Conf. on Big Data Analytics and Computational Intelligence, 2019, pp. 101–110, Doi: 10.1007/978-981-13-9643-0_10.
2. Yadav and S. Singh, “Anaemia detection using machine learning algorithms,” International Journal of Computer Applications, vol. 180, no. 40, pp. 10–15, 2018, Doi: 10.5120/ijca2018917286.
3. A. Kumar and P. Choudhury, “Predictive analytics for anaemia detection using decision trees and random forest,” Journal of Biomedical Engineering and Medical Imaging, vol. 7, no. 6, pp. 45–52, 2020, Doi: 10.14738/jbemi.76.8526.
4. S. Ahmed, M. R. Islam, and M. A. Rahman, “Using machine learning to detect anaemia: A comparative study,” Health Informatics Journal, vol. 27, no. 1, pp. 1–14, 2021, Doi: 10.1177/1460458220975594.
5. T. S. Dey and S. Mondal, “Explainable AI for anaemia classification using SHAP values,” Journal of Medical Systems, vol. 44, no. 12, p. 223, 2020, Doi: 10.1007/s10916-020-01667-5.
6. V. K. Sharma and M. Joshi, “Comparative analysis of ML algorithms for anaemia prediction,” Procedia Computer Science, vol. 167, pp. 2180–2189, 2020, Doi: 10.1016/j.procs.2020.03.269.
7. A. Gupta, R. Das, and H. Roy, “An intelligent system for anaemia prediction using ensemble learning,” International Journal of Intelligent Engineering and Systems, vol. 14, no. 5, pp. 220–230, 2021, Doi: 10.22266/ijies2021.1031.20.
8. M. Farid and A. Nasreen, “XGBoost and stacking models for improving anaemia classification accuracy,” International Journal of Healthcare Information Systems and Informatics, vol. 16, no. 1, pp. 1–24, 2021, Doi: 10.4018/IJHISI.2021010102.
9. P. Kumar and R. Sharma, “SHAP and LIME-based explainability in medical AI: Case study on anaemia,” Artificial Intelligence in Medicine, vol. 107, p. 101909, 2020, Doi: 10.1016/j.artmed.2020.101909.
10. M. A. Hossain, S. N. Nahar, and F. Rahman, “Prediction of anaemia in women using machine learning techniques,” in Proc. Int. Conf. on Big Data Analytics and Computational Intelligence, 2019, pp. 101–110, Doi: 10.1007/978-981-13-9643-0_10.
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