ARCHIVES
Original Article
An Explainable Deterministic Framework for Preventive Health Risk Stratification with Multilingual Decision Support for Low-Resource Environments
M.V. Karthikeya1
Dr. T.V. Nagalakshmi2
D. Nanda Kishore3
Mourya Sagar4
1 3 4 SRM Institute of Science and Technology, Andhra Pradesh, India. 2 Department of Basic Engineering, DVR & Dr.HS MIC College of Technology, Kanchikacherla, NTR, Andhra Pradesh, India.
Published Online: January-April 2026
Pages: 160-168
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20260501023
References
1. World Health Organization, “Obesity and overweight,” WHO Fact Sheet, 2023.
2. World Health Organization, “Body mass index – BMI classification,” WHO Guidelines, 2022.
3. American Diabetes Association, “Standards of Medical Care in Diabetes—2024,” Diabetes Care, vol. 47, no. Supplement 1, pp. S1–S350,
2024.
4. American Heart Association, “2023 AHA Guideline for the Management of Hypertension,” Circulation, vol. 148, no. 6, pp. e1–e125, 2023.
5. R. C. Detrano et al., “International application of a new probability algorithm for the diagnosis of coronary artery disease,” American Journal
of Cardiology, vol. 64, no. 5, pp. 304–310, 1989.
6. G. Octo Barnett, “The MYCIN project,” Artificial Intelligence in Medicine, vol. 1, no. 1, pp. 1–10, 1977.
7. D. W. Bates et al., “Clinical decision support systems,” BMJ, vol. 330, no. 7494, pp. 765–768, 2005.
8. L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001.
9. J. H. Friedman, “Greedy function approximation: A gradient boosting machine,” Annals of Statistics, vol. 29, no. 5, pp. 1189–1232, 2001
10. T. Chen and C. Guestrin, “XGBoost: A scalable tree boosting system,” in Proc. KDD, 2016, pp. 785–794.
11. I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press, 2016.
12. S. Lundberg and S.-I. Lee, “A unified approach to interpreting model predictions,” in Proc. NIPS, 2017, pp. 4765–4774.13. M. T. Ribeiro, S. Singh, and C. Guestrin, “Why should I trust you? Explaining the predictions of any classifier,” in Proc. KDD, 2016, pp.
1135–1144.
14. F. Doshi-Velez and B. Kim, “Towards a rigorous science of interpretable machine learning,” 2017, arXiv:1702.08608.
15. R. Guidotti et al., “A survey of methods for explaining black box models,” ACM Computing Surveys, vol. 51, no. 5, pp. 1–42, 2018.
16. J. Rajkomar et al., “Scalable and accurate deep learning with electronic health records,” npj Digital Medicine, vol. 1, no. 18, 2018.
17. E. Topol, Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Basic Books, 2019.
18. A. Esteva et al., “A guide to deep learning in healthcare,” Nature Medicine, vol. 25, pp. 24–29, 2019.
19. World Health Organization, “Global strategy on digital health 2020–2025,” WHO, 2021.
20. United Nations, “Transforming our world: The 2030 Agenda for Sustainable Development,” UN SDGs Report, 2015.
21. S. Sharma et al., “Multilingual natural language processing for healthcare applications: A survey,” IEEE Access, vol. 10, pp. 12345–12367,
2022.
22. A. Joshi et al., “Voice-based digital systems for rural healthcare access,” Information Technologies & International Development, vol. 16, pp.
45–60, 2020.
23. R. Mehta and P. Arora, “Designing inclusive digital health systems for low-literacy populations,” International Journal of Medical
Informatics, vol. 155, 2021.
24. European Commission, “Ethics guidelines for trustworthy AI,” 2019.
25. OECD, “Artificial Intelligence in Health,” OECD Digital Economy Papers, 2020.
26. S. Wachter, B. Mittelstadt, and C. Russell, “Counterfactual explanations without opening the black box,” Harvard Journal of Law &
Technology, vol. 31, no. 2, 2018.
27. M. Ahmad et al., “Explainable artificial intelligence in healthcare: A review,” Healthcare Analytics, vol. 3, 2023.
28. A. K. Das et al., “Digital health adoption in multilingual populations,” Journal of Global Health, vol. 12, 2022.
29. P. Miotto et al., “Deep patient: An unsupervised representation to predict the future of patients,” Scientific Reports, vol. 6, 2016.
30. N. Xie et al., “Explainable AI for healthcare: Methods and applications,” IEEE Reviews in Biomedical Engineering, vol. 15, pp. 102–117,
2022.
2. World Health Organization, “Body mass index – BMI classification,” WHO Guidelines, 2022.
3. American Diabetes Association, “Standards of Medical Care in Diabetes—2024,” Diabetes Care, vol. 47, no. Supplement 1, pp. S1–S350,
2024.
4. American Heart Association, “2023 AHA Guideline for the Management of Hypertension,” Circulation, vol. 148, no. 6, pp. e1–e125, 2023.
5. R. C. Detrano et al., “International application of a new probability algorithm for the diagnosis of coronary artery disease,” American Journal
of Cardiology, vol. 64, no. 5, pp. 304–310, 1989.
6. G. Octo Barnett, “The MYCIN project,” Artificial Intelligence in Medicine, vol. 1, no. 1, pp. 1–10, 1977.
7. D. W. Bates et al., “Clinical decision support systems,” BMJ, vol. 330, no. 7494, pp. 765–768, 2005.
8. L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001.
9. J. H. Friedman, “Greedy function approximation: A gradient boosting machine,” Annals of Statistics, vol. 29, no. 5, pp. 1189–1232, 2001
10. T. Chen and C. Guestrin, “XGBoost: A scalable tree boosting system,” in Proc. KDD, 2016, pp. 785–794.
11. I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press, 2016.
12. S. Lundberg and S.-I. Lee, “A unified approach to interpreting model predictions,” in Proc. NIPS, 2017, pp. 4765–4774.13. M. T. Ribeiro, S. Singh, and C. Guestrin, “Why should I trust you? Explaining the predictions of any classifier,” in Proc. KDD, 2016, pp.
1135–1144.
14. F. Doshi-Velez and B. Kim, “Towards a rigorous science of interpretable machine learning,” 2017, arXiv:1702.08608.
15. R. Guidotti et al., “A survey of methods for explaining black box models,” ACM Computing Surveys, vol. 51, no. 5, pp. 1–42, 2018.
16. J. Rajkomar et al., “Scalable and accurate deep learning with electronic health records,” npj Digital Medicine, vol. 1, no. 18, 2018.
17. E. Topol, Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Basic Books, 2019.
18. A. Esteva et al., “A guide to deep learning in healthcare,” Nature Medicine, vol. 25, pp. 24–29, 2019.
19. World Health Organization, “Global strategy on digital health 2020–2025,” WHO, 2021.
20. United Nations, “Transforming our world: The 2030 Agenda for Sustainable Development,” UN SDGs Report, 2015.
21. S. Sharma et al., “Multilingual natural language processing for healthcare applications: A survey,” IEEE Access, vol. 10, pp. 12345–12367,
2022.
22. A. Joshi et al., “Voice-based digital systems for rural healthcare access,” Information Technologies & International Development, vol. 16, pp.
45–60, 2020.
23. R. Mehta and P. Arora, “Designing inclusive digital health systems for low-literacy populations,” International Journal of Medical
Informatics, vol. 155, 2021.
24. European Commission, “Ethics guidelines for trustworthy AI,” 2019.
25. OECD, “Artificial Intelligence in Health,” OECD Digital Economy Papers, 2020.
26. S. Wachter, B. Mittelstadt, and C. Russell, “Counterfactual explanations without opening the black box,” Harvard Journal of Law &
Technology, vol. 31, no. 2, 2018.
27. M. Ahmad et al., “Explainable artificial intelligence in healthcare: A review,” Healthcare Analytics, vol. 3, 2023.
28. A. K. Das et al., “Digital health adoption in multilingual populations,” Journal of Global Health, vol. 12, 2022.
29. P. Miotto et al., “Deep patient: An unsupervised representation to predict the future of patients,” Scientific Reports, vol. 6, 2016.
30. N. Xie et al., “Explainable AI for healthcare: Methods and applications,” IEEE Reviews in Biomedical Engineering, vol. 15, pp. 102–117,
2022.
Related Articles
2026
Artificial Intelligence in Learning and Teaching
2026
Admin Assist: An AI – Driven Configuration and Orchestration for Enterprise Application
2026
Enhancing Blood Group Identification using pigeon inspired optimization: An Innovative Approach
2026
Eco-Genius: Power Up Smart, Power Down Waste
2026
Crowd-Sourced Disaster Response and Rescue Assistant
2026
