A systematic review of machine learning methods for diabetes mellitus prediction and classification in Nigeria
DOI:
https://doi.org/10.18203/2394-6040.ijcmph20251734Keywords:
Machine learning, Diabetes mellitus, Prediction, ClassificationAbstract
Diabetes mellitus (DM) is a significant public health issue in Nigeria, affecting millions of people. Early detection and management are crucial to prevent severe complications. Traditional diagnostic methods have limitations, prompting the exploration of machine learning (ML) techniques for more accurate and efficient prediction. This review systematically examines existing studies on ML applications for DM prediction and classification in Nigeria. It analyzes the research methodologies, attributes considered, study areas, and performance of different ML algorithms. The findings reveal that while ML holds promise, research is limited in scope, focusing primarily on the northern regions. Supervised learning algorithms like ANN and decision trees have demonstrated promising results for prediction and classification in Nigerian datasets, with logistic regression being a common tool for risk factor analysis. Furthermore, studies often overlook key risk factors prevalent in the southern population. This review highlights the need for future research that considers the southern population and a wider range of risk factors. It further recommends a decision support system to improve the early detection, management, and outcomes of diabetes in remote regions of Nigeria.
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References
American Diabetes Association. What is diabetes? 2023. Available at: https://diabetes.org/. Accessed on 12 April 2025.
Adebayo EF, Owolabi OD, Fagbenle OO. Prevalence and risk factors for diabetes mellitus in a semi-urban Nigerian community: A cross-sectional study. Int J Environ Res Public Health. 2020;17(13):4882.
Pradhan N, Rani G, Dhaka VS, Poonia RC. Diabetes prediction using artificial neural network. In: Deep Learning Techniques for Biomedical and Health Informatics. Elsevier. 2020;327-39. DOI: https://doi.org/10.1016/B978-0-12-819061-6.00014-8
Liu J, Tang ZH, Zeng F, Li Z, Zhou L. Artificial neural network models for prediction of cardiovascular autonomic dysfunction in general Chinese population. BMC Med Inform Decis Mak. 2013;13(1):80. DOI: https://doi.org/10.1186/1472-6947-13-80
Standl E, Khunti K, Hansen TB, Schnell O. The global epidemics of diabetes in the 21st century: Current situation and perspectives. Eur J Prev Cardiol. 2019;26(2):7-14. DOI: https://doi.org/10.1177/2047487319881021
World Health Organization. Diabetes. 2021. Available at: https://www.who.int/health-topics/ diabetes. Accessed on 12 April 2025.
Tan KR, Seng JJB, Kwan YH, Chen YJ, Zainudin SB, Loh DHF, et al. Evaluation of machine learning methods developed for prediction of diabetes complications: A systematic review. J Diabetes Sci Technol. 2023;17(2):474-89.
Buczak AL, Guven E. A survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection. IEEE Commun Surv Tutor. 2016;18(2):1153-76. DOI: https://doi.org/10.1109/COMST.2015.2494502
Witten IH, Frank E. Data mining: Practical machine learning tools and techniques. Morgan Kaufmann. 2005.
Brownlee J. Supervised and unsupervised machine learning algorithms. 2016. Available at: https://machinelearningmastery.com/supervised-and-unsupervised-machine-learning-algorithms/. Accessed on 12 April 2025.
Omar S, Ngadi A, Jebur HH. Machine Learning Techniques for Anomaly Detection. Int J Comput Appl. 2013;69(9):33-41. DOI: https://doi.org/10.5120/13715-1478
Guobing Z, Cuixia Z, Shanshan S. A Mixed Unsupervised Clustering-based Intrusion Detection Model. In: 2009 Third International Conference on Genetic and Evolutionary Computing. IEEE. 2009;402-5.
Van Engelen JE, Hoos HH. A survey on semi-supervised learning. Mach Learn. 2020;109:373-440. DOI: https://doi.org/10.1007/s10994-019-05855-6
Fujimaki R, Yairi T, Machida K. An approach to spacecraft anomaly detection problem using kernel feature space. In: Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining. ACM. 2005;401-10. DOI: https://doi.org/10.1145/1081870.1081917
Li KC, Wong BTM, Kwan R, Chan HT, Wu MMF, Cheung SKS. Evaluation of hybrid learning and teaching practices: The perspective of academics. Sustainability. 2023;15(8):6780. DOI: https://doi.org/10.3390/su15086780
Uloko AE, Musa BM, Ramalan MA, Gezawa ID, Puepet FH, Uloko AT, et al. Prevalence and risk factors for diabetes mellitus in Nigeria: A systematic review and meta-analysis. Diabetes Ther. 2018;9(3):1307-16. DOI: https://doi.org/10.1007/s13300-018-0441-1
Evwiekpaefe AE, Abdulkadir N. A predictive model for diabetes mellitus using machine learning techniques (A study in Nigeria). Afr J Inform Syst. 2023;15(1):1-21.
Dada EG, Birma AI, Gora AA. Ensemble machine learning algorithm for diabetes prediction in Maiduguri, Borno State. Mikailalsys J Math Stat. 2024;2(2):46-73. DOI: https://doi.org/10.58578/mjms.v2i2.2875
Odukoya O, Nwaneri S, Odeniyi I, Akodu B, Oluwole E, Olorunfemi G, et al. Development and comparison of three data models for predicting diabetes mellitus using risk factors in a Nigerian population. Healthc Inform Res. 2022;28(1):58-67. DOI: https://doi.org/10.4258/hir.2022.28.1.58
Muhammad LJ, Ebrahem AA, Sani SU. Predictive supervised machine learning models for diabetes mellitus. SN Comput Sci. 2020;1(4):240. DOI: https://doi.org/10.1007/s42979-020-00250-8
Uba MM, Jiadong R, Sohail MN, Irshad M, Yu K. Data mining process for predicting diabetes mellitus based model about other chronic diseases: a case study of the northwestern part of Nigeria. Healthc Technol Lett. 2019;6(4):98-102. DOI: https://doi.org/10.1049/htl.2018.5111
Tan KR, Seng JJB, Kwan YH, Chen YJ, Zainudin SB, Loh DHF, et al. Evaluation of machine learning methods developed for prediction of diabetes complications: A systematic review. J Diabetes Sci Technol. 2023;17(2):474-89. DOI: https://doi.org/10.1177/19322968211056917
Christodoulou E, Ma J, Collins GS, Steyerberg EW, Verbakel JY, Van Calster B. A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. J Clin Epidemiol. 2019;110:12-22. DOI: https://doi.org/10.1016/j.jclinepi.2019.02.004
Wolff RF, Moons KGM, Riley RD, Whiting PF, Westwood M, Collins GS, PROBAST Group. PROBAST: A tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med. 2019;170(1):51-8. DOI: https://doi.org/10.7326/M18-1376
Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. Updating guidance for reporting systematic reviews: development of the PRISMA 2020 statement. J Clin Epidemiol. 2021;134:103-12. DOI: https://doi.org/10.1016/j.jclinepi.2021.02.003
Selvin E, Parrinello CM. Age-related differences in glycaemic control in diabetes. Diabetologia. 2013;56(12):2549-51. DOI: https://doi.org/10.1007/s00125-013-3078-7
Oduoye MO, Fatima E, Muzammil MA, Dave T, Irfan H, Fariha FNU, et al. Impacts of the advancement in artificial intelligence on laboratory medicine in low- and middle-income countries: Challenges and recommendations-A literature review. Health Sci Rep. 2024;7(1):e1794. DOI: https://doi.org/10.1002/hsr2.1794
Ngusie HS, Tesfa GA, Taddese AA, Enyew EB, Alene TD, Abebe GK, et al. Predicting place of delivery choice among childbearing women in East Africa: a comparative analysis of advanced machine learning techniques. Front Public Health. 2024;12:1439320. DOI: https://doi.org/10.3389/fpubh.2024.1439320
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