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Case Study
Investigating Key Predictors and Validating a Predictive Intelligence Model for Non-Communicable Disease Risk: Case Study Kitui County, Kenya
Nicole Chepngetich1
David Muriuki Gikunju2
1 2 Department of Computer Science and Information Technology, Co-operative University of Kenya, Kenya.
Published Online: January-April 2026
Pages: 656-663
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20260501078References
1. Atun, R., Davies, J. I., Gale, E. A. M., et al. (2023). Health systems, population health and NCDs: A global perspective. The Lancet,
401(10378), 765-782.
2. Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International
Conference on Knowledge Discovery and Data Mining (pp. 785-794).
3. Creswell, J. W., & Creswell, J. D. (2020). Research design: Qualitative, quantitative and mixed methods approach (5th Ed.). SAGE
Publications.
4. GBD 2021 NCD Risk Factors Collaborators. (2024). Global burden of 87 risk factors in 204 countries and territories, 1990-2021: A
systematic analysis for the Global Burden of Disease Study 2021. The Lancet, 403(10440), 2162-2203.
5. Gichuhi, P. M., Ndungu, C. M., & Kariuki, J. (2022). Barriers to early detection of chronic diseases in Kenya: A mixed-methods
study. African Journal of Health Sciences, 35(2), 145-158.
6. Glasziou, P. P., Sanders, S. L., & Hoffmann, T. (2023). Waste in clinical AI research: A systematic review of implementation barriers. BMJ
Health & Care Informatics, 30(1), e100687.
7. Kitui County Health Department. (2023). Annual health report 2022: Chronic disease trends and health system performance. Kitui County
Government.
8. Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. In Advances in Neural Information Processing
Systems 30 (pp. 4765-4774).
9. Mabaso, S., Tambo, E., & Mutuku, M. (2024). Leveraging predictive analytics for cardiovascular disease screening in rural South
Africa. BMC Public Health, 24(1), 245-259.
10. Ministry of Health, Kenya. (2024). *Kenya Health Sector Strategic Plan 2023-2028: Progress report and updated projections*. Government
of Kenya.
11. Mohamed, S. F., Uthman, O. A., & Mwangi, J. K. (2023). Prevalence and correlates of undiagnosed hypertension and diabetes in Kenya: A
nationally representative cross-sectional study. BMJ Open, 13(4), e068742.
12. Nkengasong, J. (2020). Predictive health models in Sub-Saharan Africa: Opportunities and challenges. Science, 369(6504), 627-630.
13. Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2023). Dissecting racial bias in algorithmic predictions: Five years later. Science,
380(6642), 238-241.
14. Oketch, G., Ainebyoona, P., & Nuwagira, F. (2022). Predicting hypertension in rural Uganda: Machine learning models for community
health worker deployment. Global Health Research and Policy, 7(1), 45-58.
15. Onyango, M. K., Mwangi, J. N., & Oduor, P. (2022). Challenges in implementing predictive modeling in low-resource settings: A Kenyan
perspective. Kenya Medical Research Journal, 14(2), 112-128.
16. Pedregosa, F., Varoquaux, G., Gramfort, A., et al. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research,
12, 2825-2830.
17. Raghupathi, W., & Raghupathi, V. (2022). Machine learning in chronic disease management: Opportunities and challenges for low-resource
settings. Health Policy and Technology, 11(2), 100612.
18. Shapley, L. S. (1953). A value for n-person games. In Contributions to the Theory of Games (Vol. II, pp. 307-317). Princeton University
Press.
19. Singh, A., Dhillon, J. S., & Kumar, D. (2023). Ensemble learning for diabetes prediction in Indian populations: A comparative
study. International Journal of Medical Informatics, 170, 104952.
20. Stekhoven, D. J., & Bühlmann, P. (2012). MissForest—non-parametric missing value imputation for mixed-type data. Bioinformatics, 28(1),
112-118.
21. Wang, X., Li, Y., & Zhou, L. (2023). Gradient boosting for NCD prediction: A systematic review and meta-analysis. Journal of Epidemiology
and Community Health, 77(3), 189-197.
22. World Health Organization. (2024). *Non-communicable diseases country profiles 2024*. WHO Press.
401(10378), 765-782.
2. Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International
Conference on Knowledge Discovery and Data Mining (pp. 785-794).
3. Creswell, J. W., & Creswell, J. D. (2020). Research design: Qualitative, quantitative and mixed methods approach (5th Ed.). SAGE
Publications.
4. GBD 2021 NCD Risk Factors Collaborators. (2024). Global burden of 87 risk factors in 204 countries and territories, 1990-2021: A
systematic analysis for the Global Burden of Disease Study 2021. The Lancet, 403(10440), 2162-2203.
5. Gichuhi, P. M., Ndungu, C. M., & Kariuki, J. (2022). Barriers to early detection of chronic diseases in Kenya: A mixed-methods
study. African Journal of Health Sciences, 35(2), 145-158.
6. Glasziou, P. P., Sanders, S. L., & Hoffmann, T. (2023). Waste in clinical AI research: A systematic review of implementation barriers. BMJ
Health & Care Informatics, 30(1), e100687.
7. Kitui County Health Department. (2023). Annual health report 2022: Chronic disease trends and health system performance. Kitui County
Government.
8. Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. In Advances in Neural Information Processing
Systems 30 (pp. 4765-4774).
9. Mabaso, S., Tambo, E., & Mutuku, M. (2024). Leveraging predictive analytics for cardiovascular disease screening in rural South
Africa. BMC Public Health, 24(1), 245-259.
10. Ministry of Health, Kenya. (2024). *Kenya Health Sector Strategic Plan 2023-2028: Progress report and updated projections*. Government
of Kenya.
11. Mohamed, S. F., Uthman, O. A., & Mwangi, J. K. (2023). Prevalence and correlates of undiagnosed hypertension and diabetes in Kenya: A
nationally representative cross-sectional study. BMJ Open, 13(4), e068742.
12. Nkengasong, J. (2020). Predictive health models in Sub-Saharan Africa: Opportunities and challenges. Science, 369(6504), 627-630.
13. Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2023). Dissecting racial bias in algorithmic predictions: Five years later. Science,
380(6642), 238-241.
14. Oketch, G., Ainebyoona, P., & Nuwagira, F. (2022). Predicting hypertension in rural Uganda: Machine learning models for community
health worker deployment. Global Health Research and Policy, 7(1), 45-58.
15. Onyango, M. K., Mwangi, J. N., & Oduor, P. (2022). Challenges in implementing predictive modeling in low-resource settings: A Kenyan
perspective. Kenya Medical Research Journal, 14(2), 112-128.
16. Pedregosa, F., Varoquaux, G., Gramfort, A., et al. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research,
12, 2825-2830.
17. Raghupathi, W., & Raghupathi, V. (2022). Machine learning in chronic disease management: Opportunities and challenges for low-resource
settings. Health Policy and Technology, 11(2), 100612.
18. Shapley, L. S. (1953). A value for n-person games. In Contributions to the Theory of Games (Vol. II, pp. 307-317). Princeton University
Press.
19. Singh, A., Dhillon, J. S., & Kumar, D. (2023). Ensemble learning for diabetes prediction in Indian populations: A comparative
study. International Journal of Medical Informatics, 170, 104952.
20. Stekhoven, D. J., & Bühlmann, P. (2012). MissForest—non-parametric missing value imputation for mixed-type data. Bioinformatics, 28(1),
112-118.
21. Wang, X., Li, Y., & Zhou, L. (2023). Gradient boosting for NCD prediction: A systematic review and meta-analysis. Journal of Epidemiology
and Community Health, 77(3), 189-197.
22. World Health Organization. (2024). *Non-communicable diseases country profiles 2024*. WHO Press.
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