Application of machine learning on the prediction of diabetes screening outcomes using common symptoms in resource-poor settings
DOI:
https://doi.org/10.30681/2526101014416Keywords:
Diabetes Mellitus, eHealth, Machine Learning, Electronics, Medica, Biomedical TechnologyAbstract
Objective: to evaluate whether a machine learning algorithm could accurately predict diabetes screening outcomes using easily recognized symptoms rather than laboratory or physiological measurements. Method: de-identified patient data from Sylhet Diabetes Hospital in Bangladesh, were used. Fourteen symptoms, along with age and gender, were input into nine supervised machine learning models: Random Tree, C4.5, C-RT, CS-MC4, Linear Discriminant Analysis, Rule Induction, Decision List, ID3, and Partial Least Squares. Results: the models effectively predicted diabetes status based on symptoms, achieving an average accuracy of 94.2% ± 4, TPR of 93.4% ± 4, TNR of 95% ± 5, FPR of 4.6% ± 5, FNR of 6.6% ± 5, and an F-measure of 94.3% ± 4. Conclusion: the Random Tree algorithm performed best and shows strong potential for development into a user-friendly screening tool to encourage timely medical evaluation.
References
1. Polonsky KS. The past 200 years in diabetes. N Engl J Med. 2012; 367(14):1332-40.
2. Unnikrishnan R, Pradeepa R, Joshi SR, Mohan V. Type 2 Diabetes: Demystifying the Global Epidemic. Diabetes. 2017; 66(6):1432-42.
3. Sifunda S, Mbewu AD, Mabaso M, Manyaapelo T, Sewpaul R, Morgan JW, et al. Prevalence and Psychosocial Correlates of Diabetes Mellitus in South Africa: Results from the South African National Health and Nutrition Examination Survey (SANHANES-1). Int J Environ Res Public Health. 2023; 20(10).
4. King H, Aubert RE, Herman WH. Global burden of diabetes, 1995-2025: prevalence, numerical estimates, and projections. Diabetes Care. 1998; 21(9):1414-31.
5. Ogurtsova K, Guariguata L, Barengo NC, Ruiz PL-D, Sacre JW, Karuranga S, et al. IDF diabetes Atlas: Global estimates of undiagnosed diabetes in adults for 2021. Diabetes Res Clin Pract. 2022; 183:109118.
6. Gedebjerg A, Almdal TP, Berencsi K, Rungby J, Nielsen JS, Witte DR, et al. Prevalence of micro- and macrovascular diabetes complications at time of type 2 diabetes diagnosis and associated clinical characteristics: A cross-sectional baseline study of 6958 patients in the Danish DD2 cohort. JDC. 2018; 32(1):34-40.
7. Walker JJ, Livingstone SJ, Colhoun HM, Lindsay RS, McKnight JA, Morris AD, et al. Effect of socioeconomic status on mortality among people with type 2 diabetes: a study from the Scottish Diabetes Research Network Epidemiology Group. Diabetes Care. 2011; 34(5):1127-32.
8. Weng C, Coppini DV, Sönksen PH. Geographic and social factors are related to increased morbidity and mortality rates in diabetic patients. Diabet Med. 2000; 17(8):612-7.
9. Vijayan VV, Anjali C. Prediction and diagnosis of diabetes mellitus—A machine learning approach. 2015 IEEE Recent Advances in Intelligent Computational Systems (RAICS); 2015: IEEE.
10. Woldemichael FG, Menaria S, editors. Prediction of diabetes using data mining techniques. 2018 2nd international conference on trends in electronics and informatics (ICOEI); 2018: IEEE.
11. Tasin I, Nabil TU, Islam S, Khan R. Diabetes prediction using machine learning and explainable AI techniques. Healthc Technol Lett. 2023; 10(1-2):1-10.
12. Qin Y, Wu J, Xiao W, Wang K, Huang A, Liu B, et al. Machine Learning Models for Data-Driven Prediction of Diabetes by Lifestyle Type. Int J Environ Res Public Health. 2022; 19(22).
13. Islam MMF, Ferdousi R, Rahman S, Bushra HY. Likelihood Prediction of Diabetes at Early Stage Using Data Mining Techniques. In: Gupta M, Konar D, Bhattacharyya S, Biswas S. Computer Vision and Machine Intelligence in Medical Image Analysis. Advances in Intelligent Systems and Computing. 2020; 992.
14. Pranto B, Mehnaz SM, Mahid EB, Sadman IM, Rahman A, Momen S. Evaluating Machine Learning Methods for Predicting Diabetes among Female Patients in Bangladesh. Info. 2020; 11(8):374.
15. Ahmed N, Ahammed R, Islam MM, Uddin MA, Akhter A, Talukder MA, et al. Machine learning based diabetes prediction and development of smart web application. IJCCE. 2021; 2:229-41.
16. Islam MA, Jahan N. Prediction of onset diabetes using machine learning techniques. Int J Comput Appl. 2017; 180(5):7-11.
17. Varma KM, Panda DB. Comparative analysis of Predicting Diabetes Using Machine Learning Techniques. J Emerg Technol Innov Res. 2019; 6:522-30.
18. Rathore A, Chauhan S, Gujral S. Detecting and Predicting Diabetes Using Supervised Learning: An Approach towards Better Healthcare for Women. Int J Adv Comput Sci. 2017; 8(5).
19. Radja M, Emanuel AWR, editors. Performance evaluation of supervised machine learning algorithms using different data set sizes for diabetes prediction. 2019 5th international conference on science in information technology (ICSITech); 2019: IEEE.
20. Alghamdi T. Prediction of Diabetes Complications Using Computational Intelligence Techniques. Appl Sc. 2023; 13(5):3030.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Journal Health NPEPS
This work is licensed under a Creative Commons Attribution 4.0 International License.
Autores que publicam nesta revista concordam com os seguintes termos:
- Autores mantém os direitos autorais e concedem à revista o direito de primeira publicação, com o trabalho simultaneamente licenciado sob a Licença Creative Commons Attribution que permite o compartilhamento do trabalho com reconhecimento da autoria e publicação inicial nesta revista.
- Autores têm autorização para assumir contratos adicionais separadamente, para distribuição não-exclusiva da versão do trabalho publicada nesta revista (ex.: publicar em repositório institucional ou como capítulo de livro), com reconhecimento de autoria e publicação inicial nesta revista.
- Autores têm permissão e são estimulados a publicar e distribuir seu trabalho online (ex.: em repositórios institucionais ou na sua página pessoal) a qualquer ponto antes ou durante o processo editorial, já que isso pode gerar alterações produtivas, bem como aumentar o impacto e a citação do trabalho publicado.
Authors who publish in this journal agree to the following terms:
a) Authors retain the copyright and grant the journal the right of first publication, with the work simultaneously licensed under the Creative Commons Attribution License which allows the sharing of work with acknowledgment of authorship and initial publication in this journal.
b) Authors are authorized to take additional contracts separately, for non-exclusive distribution of the version of the work published in this journal (eg publish in institutional repository or as a book chapter), with acknowledgment of authorship and initial publication in this journal.
c) Authors are allowed and encouraged to publish and distribute their work online (eg in institutional repositories or on their personal page) at any point before or during the editorial process, as this can generate productive changes as well as increase the impact and The citation of the published work.
Los autores que publican en esta revista están de acuerdo con los siguientes términos:
a) Los autores conservan los derechos de autor y conceden el muñón derecho de la primera publicación de la obra al mismo tiempo licenciado bajo la licencia Creative Commons Atribución que permite compartir el trabajo con el reconocimiento de su publicación inicial en esta revista.
b) Los autores son capaces de asumir contratos adicionales por separado, la distribución no exclusiva de la versión del artículo publicado en esta revista (por ejemplo: Publicar en el repositorio institucional o como un libro), con un reconocimiento de su publicación inicial en esta revista.
c) Los autores están permitidos y alentados a publicar su trabajo en línea (por ejemplo: En repositorios institucionales o en su página web) en cualquier momento antes o durante el proceso de redacción, ya que puede dar lugar a intercambios productivos, así como aumentar el impacto y la citación de los trabajos publicados.
