JCEEES
Journal of Computer & Electrical and Electronics Engineering Sciences

JCEEES aims to publish original articles covering the theoretical foundations of major computer, electronic and electrical engineering sciences, as well as academic, commercial and educational aspects that propose new ideas for the application and design of artificial intelligence, software and information systems. In addition to wide-ranging regular topics, JCEEES also makes it a principle to include special topics covering specific topics in all areas of interest mainly in computational medicine, artificial intelligence, computer science, and electrical & electronic engineering science.

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Original Article
Diabetes prediction utilizing soft voting classifier
Abstract
Diabetes is one of the dangerous diseases that bring about abnormalities in blood sugar levels. Early treatment can mitigate the negative consequences of this disease. Machine learning algorithms can be leveraged to predict this disease at an early stage. In this study, a soft voting ensemble classifier approach combining random forest, AdaBoost and gradient boost algorithms is adopted to predict diabetes with the highest possible accuracy. The proposed method was tested on a publicly available dataset. The proposed approach predicted diabetes with 100% accuracy. As a result of the experiments conducted within the scope of the study, polyuria and polydipsia variables were found to be the most significant risk factors for this disease. The suggested approach outperformed similar studies in the literature.

Keywords : Diabetes prediction, machine learning, ensemble learning, soft voting classifier

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1. Alam, S., Hasan, M. K., Neaz, S., Hussain, N., Hossain, M. F., &Rahman, T. (2021). Diabetes mellitus: insights from epidemiology,biochemistry, risk factors, diagnosis, complications and comprehensivemanagement.Diabetology,2(2), 36-50.
2. Al-Haija, Q. A., Smadi, M., & Al-Bataineh, O. M. (2021). Early stagediabetes risk prediction via machine learning. InInternationalConference on Soft Computing and Pattern Recognition(pp. 451-461).Cham: Springer International Publishing.
3. Aziz, N., Akhir, E. A. P., Aziz, I. A., Jaafar, J., Hasan, M. H., & Abas, A. N. C.(2020, October). A study on gradient boosting algorithms for developmentof AI monitoring and prediction systems. In2020 International Conferenceon Computational Intelligence (ICCI)(pp. 11-16). IEEE.
4. Balaji, R., Duraisamy, R., & Kumar, M. P. (2019). Complications ofdiabetes mellitus: A review.Drug Invention Today,12(1), 98.
5. Bhat, S. S., Banu, M., Ansari, G. A., & Selvam, V. (2023). A riskassessment and prediction framework for diabetes mellitus usingmachine learning algorithms.Healthcare Analytics, 4, 100273.
6. Breiman, L. (2001). Random Forests, Machine Learning, 45(1), 5-32.
7. Chaki, J., Ganesh, S. T., Cidham, S. K., & Theertan, S. A. (2022).Machine learning and artificial intelligence-based diabetes mellitusdetection and self-management: a systematic review. J King SaudUniversity-Computer Informat Sci, 34(6), 3204-3225.
8. Dutta, A., Hasan, M. K., Ahmad, M., et al. (2022). Early prediction ofdiabetes using an ensemble of machine learning models.Int J EnvironRes Public Health. 19(19), 12378.
9. García, S., Ramírez-Gallego, S., Luengo, J., Benítez, J. M., & Herrera,F. (2016). Big data preprocessing: methods and prospects.Big DataAnalytics,1(1), 9.
10. Gündoğdu, S. (2023). Efficient prediction of early-stage diabetes usingXGBoost classifier with random forest feature selection technique.Multimed Tools Appl. 82(22), 34163-34181.
11. Islam, M. M., Ferdousi, R., Rahman, S., & Bushra, H. Y. (2020).Likelihood prediction of diabetes at early stage using data miningtechniques. InComputer vision and machine intelligence in medicalimage analysis(pp. 113-125). Springer, Singapore.
12. Laila U.E., Mahboob K, Khan AW, Khan F, & Taekeun W. (2022). AnEnsemble approach to predict early-stage diabetes risk using machinelearning: an empirical study.Sensors, 22(14), 5247.
13. Mienye, I. D., & Sun, Y. (2022). A survey of ensemble learning: concepts,algorithms, applications, and prospects. IEEE Access, 10, 99129-99149.
14. Ong, K. L., Stafford, L. K., McLaughlin, et al. (2023). Global, regional,and national burden of diabetes from 1990 to 2021, with projectionsof prevalence to 2050: a systematic analysis for the Global Burden ofDisease Study 2021.Lancet,402(10397), 203-234.
15. Pawar, S. D., Thakur, P., Radhe, B. K., Jadhav, H., Behere, V., & Pagar,V. (2017). The accuracy of polyuria, polydipsia, polyphagia, and IndianDiabetes Risk Score in adults screened for diabetes mellitus type II.MedJ Dr. DY Patil Univ,10(3), 263-267.
16. Pedregosa, F., Varoquaux, G., Gramfort, A., et al. (2011). Scikit-learn:machine learning in Python.J Machine Learning Res,12, 2825-2830.
17. Rahman, M. A., Abdulrazak, L. F., Ali, M. M., Mahmud, I., Ahmed, K.,& Bui, F. M. (2023). Machine learning-based approach for predictingdiabetes employing socio-demographic characteristics.Algorithms,16(11), 503.
18. Ruta, D., & Gabrys, B. (2005). Classifier selection for majorityvoting.Informat Fusion,6(1), 63-81.
19. Sekowski, K., Grudziaz-Sekowska, J., Pinkas, J., & Jankowski, M.(2022). Public knowledge and awareness of diabetes mellitus, its riskfactors, complications, and prevention methods among adults inPoland-A 2022 nationwide cross-sectional survey. Front Public Health.10, 1029358.
20. Sen, O., Bozkurt, K. S, & Keskin, K. (2023). Early-stage diabetesprediction using decision tree-based ensemble learning model. Int AdvRes Engineering J, 7(1), 62-71.
21. Sevinc, E. (2022). An empowered AdaBoost algorithm implementation:a COVID-19 dataset study. Comput Industrial Eng. 165, 107912.
Article available in :
Volume 2, Issue 1, 2024
Page : 31-34
https://doi.org/10.51271/JCEEES-0015
Article Information
Received : 02 Nis 2024
Accepted : 23 Nis 2024
Published : 27 Nis 2024
Corresponding Author :

Erkan Akkur: Turkish Medicines and Medical Devices Agency, Ankara, Turkiye

[email protected]
Citation : Akkur, E. (2024) Diabetes prediction utilizing soft voting classifier. J Comp Electr Electron Eng Sci, 2(1), 31-34

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