Enhancing Diabetes Classification Using a Relaxed Online Maximum Margin Algorithm
DOI:
https://doi.org/10.14421/jiska.2025.10.3.267-278Keywords:
Diabetes Classification, ROMMA, Machine Learning, Medical Diagnosis, Model EvaluationAbstract
Diabetes mellitus is a growing global health concern that requires accurate and reliable classification models for early diagnosis and effective management. Traditional machine learning models often struggle with class imbalance, generalization limitations, and high false-positive rates, leading to misdiagnoses and delayed interventions. This study enhances the Relaxed Online Maximum Margin Algorithm (ROMMA) to improve the accuracy of diabetes classification. Using a publicly available dataset from Kaggle, which contains 768 medical records with nine health attributes, the model’s performance was evaluated through a confusion matrix and classification metrics. The Enhanced ROMMA achieved an accuracy of 92%, significantly improving upon the Standard ROMMA’s 85% accuracy. The recall for diabetes detection increased from 0.83 to 0.94, reducing false negatives and ensuring more accurate patient identification. While slight misclassification still exists, this improvement enhances the model’s reliability for clinical applications. Future research should incorporate larger datasets and advanced techniques to enhance robustness and generalizability. This study contributes to the development of more accurate machine learning models for diabetes prediction, ultimately supporting better healthcare decision-making.
References
Alimbayev, A., Zhakhina, G., Gusmanov, A., Sakko, Y., Yerdessov, S., Arupzhanov, I., Kashkynbayev, A., Zollanvari, A., & Gaipov, A. (2023). Predicting 1-Year Mortality of Patients with Diabetes Mellitus in Kazakhstan Based on Administrative Health Data Using Machine Learning. Scientific Reports, 13(1), Article ID: 8412. https://doi.org/10.1038/s41598-023-35551-4
Andersen, J. K. H., Hubel, M. S., Rasmussen, M. L., Grauslund, J., & Savarimuthu, T. R. (2022). Automatic Detection of Abnormalities and Grading of Diabetic Retinopathy in 6-Field Retinal Images: Integration of Segmentation Into Classification. Translational Vision Science & Technology, 11(6), Article ID: 19. https://doi.org/10.1167/tvst.11.6.19
Arredondo, A., Azar, A., & Recamán, A. L. (2018). Diabetes, a Global Public Health Challenge with a High Epidemiological and Economic Burden on Health Systems in Latin America. Global Public Health, 13(7), 780–787. https://doi.org/10.1080/17441692.2017.1316414
Azbeg, K., Boudhane, M., Ouchetto, O., & Jai Andaloussi, S. (2022). Diabetes Emergency Cases Identification Based on a Statistical Predictive Model. Journal of Big Data, 9(1), Article ID: 31. https://doi.org/10.1186/s40537-022-00582-7
Bommer, C., Sagalova, V., Heesemann, E., Manne-Goehler, J., Atun, R., Bärnighausen, T., Davies, J., & Vollmer, S. (2018). Global Economic Burden of Diabetes in Adults: Projections From 2015 to 2030. Diabetes Care, 41(5), 963–970. https://doi.org/10.2337/dc17-1962
Christensen, D. H., Nicolaisen, S. K., Ahlqvist, E., Stidsen, J. V, Nielsen, J. S., Hojlund, K., Olsen, M. H., García-Calzón, S., Ling, C., Rungby, J., Brandslund, I., Vestergaard, P., Jessen, N., Hansen, T., Brøns, C., Beck-Nielsen, H., Sørensen, H. T., Thomsen, R. W., & Vaag, A. (2022). Type 2 Diabetes Classification: A Data-Driven Cluster Study of the Danish Centre for Strategic Research in Type 2 Diabetes (DD2) Cohort. BMJ Open Diabetes Research & Care, 10(2), Article ID: e002731. https://doi.org/10.1136/bmjdrc-2021-002731
de Wit, M., Trief, P. M., Huber, J. W., & Willaing, I. (2020). State of the Art: Understanding and Integration of the Social Context in Diabetes Care. Diabetic Medicine, 37(3), 473–482. https://doi.org/10.1111/dme.14226
Deutsch, A. J., Ahlqvist, E., & Udler, M. S. (2022). Phenotypic and Genetic Classification of Diabetes. Diabetologia, 65(11), 1758–1769. https://doi.org/10.1007/s00125-022-05769-4
Dutta, A., Hasan, Md. K., Ahmad, M., Awal, Md. A., Islam, Md. A., Masud, M., & Meshref, H. (2022). Early Prediction of Diabetes Using an Ensemble of Machine Learning Models. International Journal of Environmental Research and Public Health, 19(19), Article ID: 12378. https://doi.org/10.3390/ijerph191912378
Edeh, M. O., Khalaf, O. I., Tavera, C. A., Tayeb, S., Ghouali, S., Abdulsahib, G. M., Richard-Nnabu, N. E., & Louni, A. (2022). A Classification Algorithm-Based Hybrid Diabetes Prediction Model. Frontiers in Public Health, 10, Article ID: 829519. https://doi.org/10.3389/fpubh.2022.829519
Grant, P. J., & Marx, N. (2020). Diabetes and Cardiovascular Disease: It’s Time to Apply the Evidence. European Heart Journal. Acute Cardiovascular Care, 9(6), 586–588. https://doi.org/10.1177/2048872620952722
Hasan, Md. K., Alam, Md. A., Das, D., Hossain, E., & Hasan, M. (2020). Diabetes Prediction Using Ensembling of Different Machine Learning Classifiers. IEEE Access, 8, 76516–76531. https://doi.org/10.1109/ACCESS.2020.2989857
Herder, C., & Roden, M. (2022). A Novel Diabetes Typology: Towards Precision Diabetology from Pathogenesis to Treatment. Diabetologia, 65(11), 1770–1781. https://doi.org/10.1007/s00125-021-05625-x
Kalia, S., Saarela, O., Chen, T., O’Neill, B., Meaney, C., Gronsbell, J., Sejdic, E., Escobar, M., Aliarzadeh, B., Moineddin, R., Pow, C., Sullivan, F., & Greiver, M. (2022). Marginal Structural Models Using Calibrated Weights with SuperLearner: Application to Type II Diabetes Cohort. IEEE Journal of Biomedical and Health Informatics, 26(8), 4197–4206. https://doi.org/10.1109/JBHI.2022.3175862
Kwan, B., Fuhrer, T., Montemayor, D., Fink, J. C., He, J., Hsu, C., Messer, K., Nelson, R. G., Pu, M., Ricardo, A. C., Rincon-Choles, H., Shah, V. O., Ye, H., Zhang, J., Sharma, K., & Natarajan, L. (2023). A Generalized Covariate-Adjusted Top-Scoring Pair Algorithm with Applications to Diabetic Kidney Disease Stage Classification in the Chronic Renal Insufficiency Cohort (CRIC) Study. BMC Bioinformatics, 24(1), Article ID: 57. https://doi.org/10.1186/s12859-023-05171-w
Li, Y., & Long, P. M. (2002). The Relaxed Online Maximum Margin Algorithm. Machine Learning, 46(1–3), 361–387. https://doi.org/10.1023/A:1012435301888
Lin, X., Xu, Y., Pan, X., Xu, J., Ding, Y., Sun, X., Song, X., Ren, Y., & Shan, P.-F. (2020). Global, Regional, and National Burden and Trend of Diabetes in 195 Countries and Territories: An Analysis from 1990 to 2025. Scientific Reports, 10(1), Article ID: 14790. https://doi.org/10.1038/s41598-020-71908-9
Metsker, O., Magoev, K., Yakovlev, A., Yanishevskiy, S., Kopanitsa, G., Kovalchuk, S., & Krzhizhanovskaya, V. V. (2020). Identification of Risk Factors for Patients with Diabetes: Diabetic Polyneuropathy Case Study. BMC Medical Informatics and Decision Making, 20(1), Article ID: 201. https://doi.org/10.1186/s12911-020-01215-w
Mishra, S., Tripathy, H. K., Mallick, P. K., Bhoi, A. K., & Barsocchi, P. (2020). EAGA-MLP—An Enhanced and Adaptive Hybrid Classification Model for Diabetes Diagnosis. Sensors, 20(14), Article ID: 4036. https://doi.org/10.3390/s20144036
Peeters, F., Rommes, S., Elen, B., Gerrits, N., Stalmans, I., Jacob, J., & De Boever, P. (2023). Artificial Intelligence Software for Diabetic Eye Screening: Diagnostic Performance and Impact of Stratification. Journal of Clinical Medicine, 12(4), Article ID: 1408. https://doi.org/10.3390/jcm12041408
Phongying, M., & Hiriote, S. (2023). Diabetes Classification Using Machine Learning Techniques. Computation, 11(5), Article ID: 96. https://doi.org/10.3390/computation11050096
Rabie, O., Alghazzawi, D., Asghar, J., Saddozai, F. K., & Asghar, M. Z. (2022). A Decision Support System for Diagnosing Diabetes Using Deep Neural Network. Frontiers in Public Health, 10, Article ID: 861062. https://doi.org/10.3389/fpubh.2022.861062
Redondo, M. J., & Balasubramanyam, A. (2021). Toward an Improved Classification of Type 2 Diabetes: Lessons from Research Into the Heterogeneity of a Complex Disease. The Journal of Clinical Endocrinology & Metabolism, 106(12), e4822–e4833. https://doi.org/10.1210/clinem/dgab545
Rezaei, F., Abbasitabar, M., Mirzaei, S., Kamari Direh, Z., Ahmadi, S., Azizi, Z., & Danialy, D. (2022). Improve Data Classification Performance in Diagnosing Diabetes Using the Binary Exchange Market Algorithm. Journal of Big Data, 9(1), Article ID: 43. https://doi.org/10.1186/s40537-022-00598-z
Sadeghi, S., Khalili, D., Ramezankhani, A., Mansournia, M. A., & Parsaeian, M. (2022). Diabetes Mellitus Risk Prediction in the Presence of Class Imbalance Using Flexible Machine Learning Methods. BMC Medical Informatics and Decision Making, 22(1), Article ID: 36. https://doi.org/10.1186/s12911-022-01775-z
Sisodia, D., & Sisodia, D. S. (2018). Prediction of Diabetes Using Classification Algorithms. Procedia Computer Science, 132, 1578–1585. https://doi.org/10.1016/j.procs.2018.05.122
Torkey, H., Ibrahim, E., Hemdan, E. E.-D., El-Sayed, A., & Shouman, M. A. (2022). Diabetes Classification Application with Efficient Missing and Outliers Data Handling Algorithms. Complex & Intelligent Systems, 8(1), 237–253. https://doi.org/10.1007/s40747-021-00349-2
Wang, X., Zhai, M., Ren, Z., Ren, H., Li, M., Quan, D., Chen, L., & Qiu, L. (2021). Exploratory Study on Classification of Diabetes Mellitus Through a Combined Random Forest Classifier. BMC Medical Informatics and Decision Making, 21(1), Article ID: 105. https://doi.org/10.1186/s12911-021-01471-4
Weerahandi, H. M., Horwitz, L. I., & Blecker, S. B. (2020). Diabetes Phenotyping Using the Electronic Health Record. Journal of General Internal Medicine, 35(12), 3716–3718. https://doi.org/10.1007/s11606-020-06231-0
Wichmann, R. M., Fernandes, F. T., Chiavegatto Filho, A. D. P., Ciconelle, A. C. M., de Brito, A. M. E. S., Nunes, B. P., Silva, D. L. e, Anschau, F., de Castro Rodrigues, H., Rocha, H. A. L., dos Reis, J. C. B., de Oliveira Cavalcante, L., de Oliveira, L. P., dos Santos Andrade, L. S., Nasi, L. A., de Maria Felix, M., Mimica, M. J., de Almeida Araujo, M. E., Arnoni, M. V., … Nuno, V. L. eSant’ana. (2023). Improving the Performance of Machine Learning Algorithms for Health Outcomes Predictions in Multicentric Cohorts. Scientific Reports, 13(1), Article ID: 1022. https://doi.org/10.1038/s41598-022-26467-6
Zee, B., Lee, J., Lai, M., Chee, P., Rafferty, J., Thomas, R., & Owens, D. (2022). Digital Solution for Detection of Undiagnosed Diabetes Using Machine Learning-Based Retinal Image Analysis. BMJ Open Diabetes Research & Care, 10(6), Article ID: e002914. https://doi.org/10.1136/bmjdrc-2022-002914
Zheng, Y., Ley, S. H., & Hu, F. B. (2018). Global Aetiology and Epidemiology of Type 2 Diabetes Mellitus and Its Complications. Nature Reviews Endocrinology, 14(2), 88–98. https://doi.org/10.1038/nrendo.2017.151
Zhou, H., Myrzashova, R., & Zheng, R. (2020). Diabetes Prediction Model Based on an Enhanced Deep Neural Network. EURASIP Journal on Wireless Communications and Networking, 2020(1), Article ID: 148. https://doi.org/10.1186/s13638-020-01765-7
Zhou, L., Zheng, X., Yang, D., Wang, Y., Bai, X., & Ye, X. (2021). Application of Multi-Label Classification Models for the Diagnosis of Diabetic Complications. BMC Medical Informatics and Decision Making, 21(1), Article ID: 182. https://doi.org/10.1186/s12911-021-01525-7
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Dyan Avando Meliala, Arum Kurnia Sulistyawati, Mohammad Diqi, Marselina Endah Hiswati, Tadem Vergi Kristian
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Authors who publish with this journal agree to the following terms as stated in http://creativecommons.org/licenses/by-nc/4.0
a. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
b. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
c. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
