Comparison of SVM and Naïve Bayes Algorithms using Binary Grey Wolf Optimizer for Diabetes Mellitus Prediction

Berliana Fajrina, Safina Faradilla Hasibuan, Andi Nugroho

Abstract


Diabetes mellitus (DM) is a metabolic disorder characterized by chronically high blood sugar or glucose levels due to problems with insulin secretion, insulin response, or both. Therefore, an appropriate approach is needed in predicting diabetes to support early diagnosis and more effective prevention efforts. One of the approaches used is the machine learning method, which has proven to be capable of improving prediction accuracy. In this study, the SVM and Naïve Bayes algorithms are applied with feature selection techniques using the Binary Grey Wolf Optimizer (BGWO) to enhance classification performance. Based on the test results, SVM-BGWO showed an accuracy of 73.30% and a precision of 85.74%, while NV-BGWO achieved an accuracy of 72.60% and a precision of 84.88%. These results indicate that SVM-BGWO has superior performance compared to NV-BGWO in terms of accuracy and precision. This research aims to compare the two algorithms in order to find the most accurate model for predicting diabetes mellitus. In addition, it can help medical personnel in providing early diagnosis and enhancing diabetes prevention efforts.

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References


E. Gulshan Tokhirovna, “RISK FACTORS FOR DEVELOPING TYPE 2 DIABETES MELLITUS,” 2024. [Online]. Available: http://www.newjournal.org/

K. Arumugam, M. Naved, P. P. Shinde, O. Leiva-Chauca, A. Huaman-Osorio, and T. Gonzales-Yanac, “Multiple disease prediction using Machine learning algorithms,” Mater Today Proc, vol. 80, pp. 3682–3685, Jan. 2023, doi: 10.1016/j.matpr.2021.07.361.

M. I. Akazue, G. A. Nwokolo, O. A. Ejaita, C. O. Ogeh, and E. Ufiofio, “Machine Learning Survival Analysis Model for Diabetes Mellitus,” 2023. [Online]. Available: www.ijisrt.com754

B. Priambodo, R. A. Kadir, and A. Ahmad, “Clustering Urban Roads Using Local Binary Patterns to Enhance the Accuracy of Traffic Flow Prediction,” vol. 14, no. 5, 2024.

D. Shaw, “Review of Grey Wolf Optimizer,” 2024, doi: 10.13140/RG.2.2.14111.57763.

A. Bilal, A. Imran, T. I. Baig, X. Liu, E. Abouel Nasr, and H. Long, “Breast cancer diagnosis using support vector machine optimized by improved quantum inspired grey wolf optimization,” Sci Rep, vol. 14, no. 1, Dec. 2024, doi: 10.1038/s41598-024-61322-w.

A. Amali, D. Maulana, E. Widodo, A. Firmansyah, and M. Danny, “Optimizing Sentiment Analysis of Bekasi Flood Management Using SVM and Naive Bayes with Advanced Feature Selection,” Brilliance: Research of Artificial Intelligence, vol. 4, no. 1, pp. 362–371, Jul. 2024, doi: 10.47709/brilliance.v4i1.4268.

E. ÜLKER and I. M. Nur, “A Novel Hybrid IoT Based IDS Using Binary Grey Wolf Optimizer (BGWO) and Naive Bayes (NB),” European Journal of Science and Technology, Oct. 2020, doi: 10.31590/ejosat.804113.

D. Tomic, J. E. Shaw, and D. J. Magliano, “The burden and risks of emerging complications of diabetes mellitus,” Sep. 01, 2022, Nature Research. doi: 10.1038/s41574-022-00690-7.

R. Bilous, R. Donnely, and I. Idris, “Hanbook of Diabetes,” 2021.

C. A. Whicher, S. O’Neill, and R. I. G. Holt, “Diabetes in the UK: 2019,” Diabetic Medicine, vol. 37, no. 2, pp. 242–247, Feb. 2020, doi: 10.1111/dme.14225.

P. Saeedi et al., “Mortality attributable to diabetes in 20–79 years old adults, 2019 estimates: Results from the International Diabetes Federation Diabetes Atlas, 9th edition,” Diabetes Res Clin Pract, vol. 162, Apr. 2020, doi: 10.1016/j.diabres.2020.108086.

M. K. Gupta and P. Chandra, “A comprehensive survey of data mining,” International Journal of Information Technology (Singapore), vol. 12, no. 4, pp. 1243–1257, Dec. 2020, doi: 10.1007/s41870-020-00427-7.

R. Verma, V. Nagar, and S. Mahapatra, “THE COMMENCEMENT OF MACHINE LEARNING SOLICITATION TO BIOINFORMATICS,” 2021.

D. S. Watson, “On the Philosophy of Unsupervised Learning,” Philos Technol, vol. 36, no. 2, Jun. 2023, doi: 10.1007/s13347-023-00635-6.

D. Wang, Y. Ji, H. Wang, and M. Huang, “Binary grey wolf optimizer with a novel population adaptation strategy for feature selection,” IET Control Theory and Applications, vol. 17, no. 17, pp. 2313–2331, Nov. 2023, doi: 10.1049/cth2.12498.

P. Hu, J. S. Pan, and S. C. Chu, “Improved Binary Grey Wolf Optimizer and Its application for feature selection,” Knowl Based Syst, vol. 195, May 2020, doi: 10.1016/j.knosys.2020.105746.

Y. Yu et al., “Quantitative analysis of multiple components based on support vector machine (SVM),” Optik (Stuttg), vol. 237, Jul. 2021, doi: 10.1016/j.ijleo.2021.166759.

K. Harimoorthy and M. Thangavelu, “Multi-disease prediction model using improved SVM-radial bias technique in healthcare monitoring system,” Mar. 01, 2021, Springer Science and Business Media Deutschland GmbH. doi: 10.1007/s12652-019-01652-0.

M. R. Islam, S. Banik, K. N. Rahman, and M. M. Rahman, “A comparative approach to alleviating the prevalence of diabetes mellitus using machine learning,” Computer Methods and Programs in Biomedicine Update, vol. 4, Jan. 2023, doi: 10.1016/j.cmpbup.2023.100113.

V. Chang, J. Bailey, Q. A. Xu, and Z. Sun, “Pima Indians diabetes mellitus classification based on machine learning (ML) algorithms,” Neural Comput Appl, vol. 35, no. 22, pp. 16157–16173, Aug. 2023, doi: 10.1007/s00521-022-07049-z.


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