Tamara Suzyo Zimba

A Final Year Research Project submitted in partial fulfilment of the requirements for the degree of Master of Science in Information Technology

DOI : https://doi.org/10.58806/ijsshmr.2026.v5i5n07

Google Scholar Download Pdf
ABSTRACT

This research presents the development and implementation of a Machine Learning Decision Support System (ML-DSS) aimed at enhancing child health protection in Zambia. The system utilizes a predictive framework based on a star-schema database architecture, which includes a fact table containing child-level data linked to various health and educational indicators. Specifically, the ML-DSS focuses on binary classification tasks to assess school dropout risks and stunting risks among children, employing deep learning techniques facilitated by TensorFlow. Key results highlight the model’s performance metrics, demonstrating its potential to inform early interventions in child health and education. The research identifies critical factors influencing dropout rates and stunting, emphasizing the significance of nutrition and school attendance. Despite limitations, including the absence of detailed household financial data, the model provides a robust tool for NGOs to enhance their programming and improve child health outcomes.

KEYWORDS:

Machine Learning Decision Support System (ML-DSS), Child health protection, Star-schema database, Predictive framework, Binary classification, School dropout risk, Stunting risk, TensorFlow Deep Learning, Fact & Dimension Tables, Early intervention, NGOs programming, Data limitations, Zambia context, Feature engineering, Model evaluation

REFERENCES

1) S. M. D. A. C. Jayatilake and G. U. Ganegoda, “Involvement of Machine Learning Tools in Healthcare Decision Making,” Journal of Healthcare Engineering, vol. 2021, Article ID 6679512, 20 pages, Jan. 27, 2021.

2) Y. Du, C. McNestry, L. Wei, A. M. Antoniadi, F. M. McAuliffe, and C. Mooney, “Machine learning-based clinical decision support systems for pregnancy care: A systematic review,” Int. J. Med. Inform., vol. 173, no. 105040, Mar. 2023.

3) Awaysheh, J. Wilcke, F. Elvinger, L. Rees, W. Fan, and K. L. Zimmerman, “Review of Medical Decision Support and Machine-Learning Methods,” Veterinary Pathology, vol. 56, no. 4, pp. 512–525, 2019, doi: 10.1177/0300985819829524.

4) J. Y. Lam, C. Shimizu, A. H. Tremoulet, E. Bainto, S. C. Roberts, N. Sivilay, M. A. Gardiner, J. T. Kanegaye, A. H. Hogan, J. C. Salazar, S. Mohandas, J. R. Szmuszkovicz, S. Mahanta, A. Dionne, J. W. Newburger, E. Ansusinha, R. L. DeBiasi, S. Hao, X. B. Ling, H. J. Cohen, S. Nemati, and J. C. Burns, “A machine-learning algorithm for diagnosis of multisystem inflammatory syndrome in children and Kawasaki disease in the USA: a retrospective model development and validation study,” Lancet Digit. Health, vol. 4, pp. e717–e726, Oct. 2022.

5) K. A. McCord-De Iaco, F. Gesualdo, E. Pandolfi, I. Croci, and A. E. Tozzi, “Machine learning clinical decision support systems for surveillance: a case study on pertussis and RSV in children,” Front. Pediatr., vol. 11, Art. no. 1112074, 2023, doi: 10.3389/fped.2023.1112074.

6) D. Ravi, C. Wong, F. Deligianni, M. Berthelot, J. Andreu-Perez, B. Lo, and G.-Z. Yang, “Deep learning for health informatics,” IEEE Journal of Biomedical and Health Informatics, vol. 21, no. 1, pp. 4–21, Jan. 2017, doi: 10.1109/JBHI.2016.2636665.

7) Ba, “How to measure monitoring and evaluation system effectiveness?”, African Evaluation Journal, vol. 9, no. 1, Art. no. a553, Sep. 2021, doi: 10.4102/aej.v9i1.553.

8) SOAS Centre for Development, Environment and Policy, “Unit Ten: Monitoring and Evaluation,” P534 Project Planning and Management, School of Oriental and African Studies, University of London, n.d.

9) W. H. North, “Designing Monitoring and Evaluation Systems: Issues and Opportunities,” Occasional Paper, Center for Development Information and Evaluation, Bureau for Program and Policy Coordination, U.S. Agency for International Development, Washington, DC, USA, Sept. 1987.

10) K. C. Lai, J. Hancock, and D. Muller-Praefcke, “Stocktaking of M&E and Management Information Systems: Selected agricultural and rural development projects in South Asia,” FAO/World Bank Cooperative Programme, Food and Agriculture Organization of the United Nations, Rome, n.d. T. Barton, “Guidelines to monitoring and evaluation: How are we doing ?,” CARE-Uganda, 1997.

11) W. H. DeLone and E. R. McLean, “The DeLone and McLean Model of information systems success: A ten-year update,” Journal of Management Information Systems, vol. 19, no. 4, pp. 9–30, 2003.

12) C. Edmondson and S. E. McManus, “Methodological fit in management field research,” Academy of Management Review, vol. 32, no. 4, pp. 1155–1179, 2007.

13) D. Nicolini, “Practice as the site of knowing: Insights from the field of telemedicine,” Organization Science, vol. 22, no. 3, pp. 602–620, 2011.

14) Y. D. Du, C. McNestry, L. Wei et al., “Revolutionary improvements in healthcare process through deep learning,” International Journal of Medical Informatics, vol. 173, p. 105040, 2023.

15) T. Antoniadi et al., “Explainable AI for clinical decision support: A review,” [Conference/Journal], 20XX. (Exact venue/details not specified in excerpt)

16) J. Wellwood, S. Johannessen, and D. Spiegelhalter, “How does computer-aided diagnosis improve the management of acute abdominal pain?,” Ann. R. Coll. Surg. Engl., vol. 74, no. 1, pp. 40–46, 1992.

17) B. J. Wielinga, A. T. Schreiber, and J. A. Breuker, “KADS: a modelling approach to knowledge engineering,” Knowledge Acquisition, vol. 4, no. 1, pp. 5–53, 1992.

18) J. L. Willems, C. Abreu-Lima, P. Arnaud et al., “The diagnostic performance of computer programs for the interpretation of electrocardiograms,” N. Engl. J. Med., vol. 325, no. 25, pp. 1767–1773, 1991.

19) D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” Nature, vol. 323, pp. 533–536, 1986.

20) N. Williams, S. Zander, and G. Armitage, “A preliminary performance comparison of five machine learning algorithms for practical IP traffic flow classification,” in Proc. ACM SIGCOMM, vol. 36, no. 5, pp. 5–16, 2006.

21) H. Witten, E. Frank, and M. A. Hall, Data Mining: Practical Machine Learning Tools and Techniques. Amsterdam, NL: Elsevier/Morgan Kaufmann, 2011.

22) H. Wu, M. D. Gordon, and W. Fan, “Collective taxonomizing: a collaborative approach to organizing document repositories,” Decision Support Systems, vol. 50, no. 1, pp. 292–303, 2010.

23) W. J. Wu, S. W. Lin, and W. K. Moon, “An artificial immune system-based support vector machine approach for classifying ultrasound breast tumor images,” J. Digit. Imaging, vol. 28, no. 5, pp. 576–585, 2015.

24) D. Xhemali, C. J. Hinde, and R. G. Stone, “Naive Bayes vs. decision trees vs. neural networks in the classification of training web pages,” IJCSI Int. J. Comput. Sci. Issues, vol. 4, no. 1, pp. 16–23, 2009.

25) E. I. Zacharaki, V. G. Kanas, and C. Davatzikos, “Investigating machine learning techniques for MRI-based classification of brain neoplasms,” Int. J. Comput. Assist. Radiol. Surg., vol. 6, no. 6, pp. 821–828, 2011.

26) D. Zeldis and S. Prescott, “Fish disease diagnosis program—problems and some solutions,” Aquacultural Eng., vol. 23, nos. 1–3, pp. 3–11, 2000.

27) Zelic, I. Kononenko, N. Lavrač et al., “Induction of decision trees and Bayesian classification applied to diagnosis of sport injuries,” J. Med. Syst., vol. 21, no. 6, pp. 429–444, 1997.

28) X. Zhang, B. Hu, X. Ma et al., “Ontology driven decision support for the diagnosis of mild cognitive impairment,” Comput. Methods Programs Biomed., vol. 113, no. 3, pp. 781–791, 2014.

Cite this Article:Zimba, T. S. (2026). Enhanced Child Health & Social Protection through a Machine Learning Decision Support System. INTERNATIONAL JOURNAL OF SOCIAL SCIENCE HUMANITY & MANAGEMENT RESEARCHS, 5(5), 937-976. https://doi.org/10.58806/ijsshmr.2026.v5i5n07

VOlUME 05 ISSUE 05 May 2026