Journal of Preventive Medicine and Public Health

  • 제56권1호
  • /
  • Pages.41-49
  • /
  • 2023
  • /
  • 1975-8375(pISSN)
  • /
  • 2233-4521(eISSN)
대한예방의학회 (The Korean Society for Preventive Medicine)
권호 표지
DOI QR코드

DOI QR Code

Prediction of Stunting Among Under-5 Children in Rwanda Using Machine Learning Techniques

  • 투고 : 2022.08.30
  • 심사 : 2022.12.15
  • 발행 : 2023.01.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Objectives: Rwanda reported a stunting rate of 33% in 2020, decreasing from 38% in 2015; however, stunting remains an issue. Globally, child deaths from malnutrition stand at 45%. The best options for the early detection and treatment of stunting should be made a community policy priority, and health services remain an issue. Hence, this research aimed to develop a model for predicting stunting in Rwandan children. Methods: The Rwanda Demographic and Health Survey 2019-2020 was used as secondary data. Stratified 10-fold cross-validation was used, and different machine learning classifiers were trained to predict stunting status. The prediction models were compared using different metrics, and the best model was chosen. Results: The best model was developed with the gradient boosting classifier algorithm, with a training accuracy of 80.49% based on the performance indicators of several models. Based on a confusion matrix, the test accuracy, sensitivity, specificity, and F1 were calculated, yielding the model's ability to classify stunting cases correctly at 79.33%, identify stunted children accurately at 72.51%, and categorize non-stunted children correctly at 94.49%, with an area under the curve of 0.89. The model found that the mother's height, television, the child's age, province, mother's education, birth weight, and childbirth size were the most important predictors of stunting status. Conclusions: Therefore, machine-learning techniques may be used in Rwanda to construct an accurate model that can detect the early stages of stunting and offer the best predictive attributes to help prevent and control stunting in under five Rwandan children.

키워드

과제정보

This research could not have been completed without the contribution of different people who deserve acknowledgement: Deborah Mukandamage, Diane Uwizera, Prosper Twiringiyimana, and the African Centre of Excellence in Data Science at the University of Rwanda

참고문헌

  1. World Health Organization. Levels and trends in child malnutrition: UNICEF/WHO/the World Bank Group joint child malnutrition estimates: key findings of the 2020 edition; 2020 [cited 2022 Aug 1]. Available from: https://www.who.int/publications/i/item/9789240003576.
  2. Skoufias E, Vinha K, Sato R. Reducing stunting through multisectoral efforts in sub-Saharan Africa. J Afr Econ 2021;30(4): 324-348. https://doi.org/10.1093/jae/ejaa016
  3. Skoufias E. All hands on deck: halting the vicious circle of stunting in sub-Saharan Africa; 2018 [cited 2021 Dec 19]. Available from: https://blogs.worldbank.org/health/all-hands-deck-halting-vicious-circle-stunting-sub-saharan-africa.
  4. National Institute of Statistics of Rwanda. Demographic and Health Survey (2019/20); 2021 [cited 2022 Aug 1]. Available from: https://www.statistics.gov.rw/datasource/demographic-and-health-survey-201920.
  5. Weatherspoon DD, Miller S, Ngabitsinze JC, Weatherspoon LJ, Oehmke JF. Stunting, food security, markets and food policy in Rwanda. BMC Public Health 2019;19(1):882.
  6. Wamani H, Astrom AN, Peterson S, Tumwine JK, Tylleskar T. Boys are more stunted than girls in sub-Saharan Africa: a meta-analysis of 16 demographic and health surveys. BMC Pediatr 2007;7:17.
  7. Dang S, Yan H, Yamamoto S. High altitude and early childhood growth retardation: new evidence from Tibet. Eur J Clin Nutr 2008;62(3):342-348. https://doi.org/10.1038/sj.ejcn.1602711
  8. Binagwaho A, Agbonyitor M, Rukundo A, Ratnayake N, Ngabo F, Kayumba J, et al. Underdiagnosis of malnutrition in infants and young children in Rwanda: implications for attainment of the Millennium Development Goal to end poverty and hunger. Int J Equity Health 2011;10(1):61.
  9. Khare S, Kavyashree S, Gupta D, Jyotishi A. Investigation of nutritional status of children based on machine learning techniques using Indian Demographic and Health Survey data. Procedia Comput Sci 2017;115:338-349. https://doi.org/10.1016/j.procs.2017.09.087
  10. Momand Z, Mongkolnam P, Kositpanthavong P, Chan JH. Data mining based prediction of malnutrition in Afghan children. In: 12th International Conference on Knowledge and Smart Technology (KST). Pattaya: IEEE; 2020, p. 12-17.
  11. Agrawal A, Agrawal H, Mittal S, Sharma M. Disease prediction using machine learning. In: Proceedings of 3rd International Conference on Internet of Things and Connected Technologies (ICIoTCT) [cited 2022 Aug 1]. Available from: http://dx.doi.org/10.2139/ssrn.3167431.
  12. World Health Organization. Guideline: assessing and managing children at primary health-care facilities to prevent overweight and obesity in the context of the double burden of malnutrition; 2017 [cited 2022 Aug 1]. Available from: https://www.who.int/publications/i/item/9789241550123.
  13. MIT Critical Data. Secondary analysis of electronic health records; 2016 [cited 2023 Jan 20]. Available from: https://www.ncbi.nlm.nih.gov/books/NBK543630/.
  14. Jonsson P, Wohlin C. An evaluation of k-nearest neighbour imputation using Likert data. In: 10th International Symposium on Software Metrics, 2004. Chicago: IEEE; 2004, p. 108-118.
  15. Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 2002;16:321-357. https://doi.org/10.1613/jair.953
  16. Bitew FH, Sparks CS, Nyarko SH. Machine learning algorithms for predicting undernutrition among under-five children in Ethiopia. Public Health Nutr 2022;25(2):269-280.
  17. Berrar D. Cross-validation. Encycl Bioinform Comput Biol 2019;1:542-545. https://doi.org/10.1016/B978-0-12-809633-8.20349-X
  18. Sulistiani H, Tjahyanto A. Comparative analysis of feature selection method to predict customer loyalty. IPTEK J Eng 2017;3(1):1-5. https://doi.org/10.12962/joe.v3i1.2257
  19. Sidey-Gibbons JA, Sidey-Gibbons CJ. Machine learning in medicine: a practical introduction. BMC Med Res Methodol 2019;19(1):64.
  20. Lumbanraja FR, Fitri E, Junaidi A, Prabowo R. Abstract classification using support vector machine algorithm (case study: abstract in a Computer Science Journal). 2021 J Phys Conf Ser 2021;1751(1):012042.
  21. Lachiche N. Receiver operating characteristic (ROC) analysis. In: John W, editor. Encyclopedia of data warehousing and mining. 2nd ed. Hershey: IGI Global; 2017, p. 1675-1681.
  22. Sun C, Wang X, Xu J. Study on feature selection in finance text categorization. In: 2009 IEEE International Conference on Systems, Man and Cybernetics. 2009 Oct 11-14; San Antonio: IEEE; 2009, p. 5077-5082.
  23. Khan JR, Tomal JH, Raheem E. Model and variable selection using machine learning methods with applications to childhood stunting in Bangladesh. Inform Health Soc Care 2021; 46(4):425-442. https://doi.org/10.1080/17538157.2021.1904938
  24. Saleh A, Syahrul S, Hadju V, Andriani I, Restika I. Role of maternal in preventing stunting: a systematic review. Gac Sanit 2021; 35 Suppl 2:S576-S582. https://doi.org/10.1016/j.gaceta.2021.10.087
  25. Jusoff K, Sahimi NN. Television and media literacy in young children: issues and effects in early childhood. Int Educ Stud 2009;2(3):151-157. https://doi.org/10.5539/ies.v2n3p151
  26. Makoka D. The impact of maternal education on child nutrition: evidence from Malawi, Tanzania, and Zimbabwe; 2013 [cited 2022 Aug 1]. Available from: https://www.dhsprogram. com/pubs/pdf/WP84/WP84.pdf.
  27. Nshimyiryo A, Hedt-Gauthier B, Mutaganzwa C, Kirk CM, Beck K, Ndayisaba A, et al. Risk factors for stunting among children under five years: a cross-sectional population-based study in Rwanda using the 2015 Demographic and Health Survey. BMC Public Health 2019;19(1):175.