Analisis dan Optimalisasi Performa Algoritma Gaussian Naive Bayes pada Prediksi Metabolic Syndrome Menggunakan SMOTE
DOI:
https://doi.org/10.14421/jiska.2024.9.2.112-122Keywords:
Metabolic Syndrome, Machine learning, Gaussian Naive Bayes, Synthetic Minority Over-sampling Technique (SMOTE), PredictionAbstract
Metabolic syndrome is a complex global health problem, with symptoms such as abdominal obesity, insulin resistance, high blood pressure, high blood sugar, and abnormal blood lipids. With this global challenge, several studies have attempted to predict these diseases using machine learning methods. However, often, predictions about a disease result in data imbalance where minority classes are underrepresented. To balance the class proportions, the Synthetic Minority Over-sampling Technique (SMOTE) method replicates the minority class samples. In this research, the technique applied to predict is the Gaussian Naive Bayes (GNB) algorithm. The results show an increase in prediction accuracy by 0.2 from 0.81 to 0.83. This study confirms the critical role of the SMOTE oversampling method in machine learning using the Gaussian Naive Bayes (GNB) algorithm in Metabolic Syndrome prediction and its positive impact on diagnostic efficiency and public health.
References
Anand, M. V., KiranBala, B., Srividhya, S. R., C., K., Younus, M., & Rahman, M. H. (2022). Gaussian Naïve Bayes Algorithm: A Reliable Technique Involved in the Assortment of the Segregation in Cancer. Mobile Information Systems, 2022, 1–7. https://doi.org/10.1155/2022/2436946
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research, 16, 321–357. https://doi.org/10.1613/jair.953
Dobrowolski, P., Prejbisz, A., Kuryłowicz, A., Baska, A., Burchardt, P., Chlebus, K., Dzida, G., Jankowski, P., Jaroszewicz, J., Jaworski, P., Kamiński, K., Kapłon-Cieślicka, A., Klocek, M., Kukla, M., Mamcarz, A., Mastalerz-Migas, A., Narkiewicz, K., Ostrowska, L., Śliż, D., … Bogdański, P. (2022). Metabolic syndrome – a new definition and management guidelines A joint position paper by the Polish Society of Hypertension, Polish Society for the Treatment of Obesity, Polish Lipid Association, Polish Association for Study of Liver, Polish Society of Family Medicine, Polish Society of Lifestyle Medicine, Division of Prevention and Epidemiology Polish Cardiac Society, “Club 30” Polish Cardiac Society, and Division of Metabolic and Bariatric Surgery Society of Polish Surgeons. Archives of Medical Science, 18(5), 1133–1156. https://doi.org/10.5114/aoms/152921
Han, T. S., & Lean, M. E. (2016). A clinical perspective of obesity, metabolic syndrome and cardiovascular disease. JRSM Cardiovascular Disease, 5, 204800401663337. https://doi.org/10.1177/2048004016633371
Herningtyas, E. H., & Ng, T. S. (2019). Prevalence and distribution of metabolic syndrome and its components among provinces and ethnic groups in Indonesia. BMC Public Health, 19(1), 1–12. https://doi.org/10.1186/S12889-019-6711-7/FIGURES/3
Hu, X., Li, X.-K., Wen, S., Li, X., Zeng, T.-S., Zhang, J.-Y., Wang, W., Bi, Y., Zhang, Q., Tian, S.-H., Min, J., Wang, Y., Liu, G., Huang, H., Peng, M., Zhang, J., Wu, C., Li, Y.-M., Sun, H., … Chen, L.-L. (2022). Predictive modeling the probability of suffering from metabolic syndrome using machine learning: A population-based study. Heliyon, 8(12), e12343. https://doi.org/10.1016/j.heliyon.2022.e12343
Huang, P. L. (2009). A comprehensive definition for metabolic syndrome. Disease Models & Mechanisms, 2(5–6), 231–237. https://doi.org/10.1242/dmm.001180
Libnao, M., Misula, M., Andres, C., Mariñas, J., & Fabregas, A. (2023). Traffic incident prediction and classification system using naïve bayes algorithm. Procedia Computer Science, 227, 316–325. https://doi.org/10.1016/j.procs.2023.10.530
Palaniappan, L. P., Wong, E. C., Shin, J. J., Fortmann, S. P., & Lauderdale, D. S. (2011). Asian Americans have greater prevalence of metabolic syndrome despite lower body mass index. International Journal of Obesity, 35(3), 393–400. https://doi.org/10.1038/ijo.2010.152
Rochlani, Y., Pothineni, N. V., Kovelamudi, S., & Mehta, J. L. (2017). Metabolic syndrome: Pathophysiology, management, and modulation by natural compounds. Therapeutic Advances in Cardiovascular Disease, 11(8), 215–225. https://doi.org/10.1177/1753944717711379/ASSET/IMAGES/LARGE/10.1177_1753944717711379-FIG1.JPEG
Saklayen, M. G. (2018). The Global Epidemic of the Metabolic Syndrome. Current Hypertension Reports, 20(2), 1–8. https://doi.org/10.1007/S11906-018-0812-Z/METRICS
Tavares, L. D., Manoel, A., Donato, T. H. R., Cesena, F., Minanni, C. A., Kashiwagi, N. M., da Silva, L. P., Amaro, E., & Szlejf, C. (2022). Prediction of metabolic syndrome: A machine learning approach to help primary prevention. Diabetes Research and Clinical Practice, 191, 110047. https://doi.org/10.1016/j.diabres.2022.110047
The GBD 2015 Obesity Collaborators. (2017). Health Effects of Overweight and Obesity in 195 Countries over 25 Years. New England Journal of Medicine, 377(1), 13–27. https://doi.org/10.1056/NEJMOA1614362/SUPPL_FILE/NEJMOA1614362_DISCLOSURES.PDF
Venkata, P., & Pandya, V. (2022). Data mining model and Gaussian Naive Bayes based fault diagnostic analysis of modern power system networks. Materials Today: Proceedings, 62(P13), 7156–7161. https://doi.org/10.1016/j.matpr.2022.03.035
Wilkinson, M. J., Manoogian, E. N. C., Zadourian, A., Lo, H., Fakhouri, S., Shoghi, A., Wang, X., Fleischer, J. G., Navlakha, S., Panda, S., & Taub, P. R. (2020). Ten-Hour Time-Restricted Eating Reduces Weight, Blood Pressure, and Atherogenic Lipids in Patients with Metabolic Syndrome. Cell Metabolism, 31(1), 92-104.e5. https://doi.org/10.1016/j.cmet.2019.11.004
World Health Organization. (2020). The top 10 causes of death. World Health Organization. https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death
Zhou, Y., Wu, T., Jiang, Y., Li, Y., Li, K., Quan, L., & Lyu, Q. (2022). DeepNup: Prediction of Nucleosome Positioning from DNA Sequences Using Deep Neural Network. Genes, 13(11), 1983. https://doi.org/10.3390/genes13111983
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