Development and Performance Analysis of Machine Learning Methods for Predicting Metabolic Syndrome Among Postmenopausal Women of India
Development and Performance Analysis of Machine Learning Methods for Predicting Metabolic Syndrome
Abstract
Aim: The objective of this study is to develop and evaluate the performance of machine learning methods for predicting metabolic syndrome among postmenopausal women in India. Methods: This work uses supervised machine-learning to construct a system that achieves notable accuracy. By assessing several factors, including as accuracy, sensitivity, specificity, precision, recall, F-Measure, Receiver Operating Characteristic (ROC), Precision–Recall Curve (PRC), and Area Under the Curve (AUC), several classification methods are used to identify the best-performing classifier. Result: The prevalence of MetS among postmenopausal women was found to be 40.17%, with 19.21% of respondents exhibiting a hyperglycaemic state and 57.86% having low HDL-C levels. In the Indian setting, among the machine learning algorithms tested, the Decision Tree and Random Forest classifiers emerged as the best-performing models, achieving an accuracy of 90.22%. These models utilized the six most essential features as identified by the International Diabetes Federation (IDF). Key predictive factors included waist circumference (WC), serum triglyceride levels (TG), and fasting blood sugar (FBS). Conclusion: This study will play a crucial role in predicting MetS and improving the quality of life for neglected post-menopausal women. Various software like web and mobile applications can adopt this paradigm. Swift diagnosis will lower the costs of diagnosis and further complications.
Keywords:
Machine Learning, Metabolic Syndrome, India, Postmenopausal Women
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References
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Ghosh, J., Chaudhuri, D., Saha, I., & Chaudhuri, A. N. (2020). Prevalence of metabolic syndrome, vitamin D level, and their association among elderly women in a rural community of West Bengal, India. Medical Journal of Dr. DY Patil Vidyapeeth, 13(4), 315-320. https://journals.lww.com/mjdy/toc/2020/13040
Ghosh, J., Chaudhuri, D., Saha, I., & Nag Chaudhuri, A. (2022b). Association of conicity index with different cardiovascular disease risk factors among the rural elderly women of West Bengal, India. Indian Journal of Community Medicine, 47, 18–22. https://pmc.ncbi.nlm.nih.gov/articles/PMC8971865/
Ghosh, J., Singh, K., Choudhury, S. R., Koner, S., Basu, N., & Maity, S. (2022a). Impact of diet and nutrition on memory T cell development, maintenance, and function in the context of a healthy immune system. Acta Scientific Nutritional Health, 6(8), 142–154. http://dx.doi.org/10.31080/ASNH.2022.06.1108
Gimpy, M. D. R. V. (2014). Missing value imputation in multi attribute data set. International Journal of Computer Science and Information Technology, 5(4), 1-7. https://api.semanticscholar.org/CorpusID:14050728
Gutiérrez-Esparza, G. O., Infante Vázquez, O., Vallejo, M., & Hernández-Torruco, J. (2020). Prediction of metabolic syndrome in a Mexican population applying machine learning algorithms. Symmetry, 12(4). https://doi.org/10.3390/sym12040581
Hasan, S. M. M., Mamun, M. A., Uddin, M. P., & Hossain, M. A. (2018). Comparative analysis of classification approaches for heart disease prediction. In 2018 International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2) (pp. 1-4). IEEE. https://api.semanticscholar.org/CorpusID:52299844
Hosseini-Esfahani, F., Alafchi, B., Cheraghi, Z., Doosti-Irani, A., Mirmiran, P., Khalili, D., & Azizi, F. (2021). Using machine learning techniques to predict factors contributing to the incidence of metabolic syndrome in Tehran: Cohort study. JMIR Public Health and Surveillance, 7(4). https://doi.org/10.2196/27304
Janssen, I., Powell, L. H., Crawford, S., Lasley, B., & Sutton-Tyrrell, K. (2008). Menopause and the metabolic syndrome: The Study of Women's Health Across the Nation. Archives of Internal Medicine, 168(14), 1568-1575. https://doi.org/10.1001/archinte.168.14.1568
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Karimi-Alavijeh, F., Jalili, S., & Sadeghi, M. (2016). Predicting metabolic syndrome using decision tree and support vector machine methods. ARYA Atherosclerosis, 12(3), 146-152. https://pubmed.ncbi.nlm.nih.gov/27752272/
Krstajic, D., Buturovic, L. J., Leahy, D. E., & Thomas, S. (2014). Cross-validation pitfalls when selecting and assessing regression and classification models. Journal of Cheminformatics, 6(1), 10. https://doi.org/10.1186/1758-2946-6-10
Lee, S., Lee, H., Choi, J. R., & Koh, S. B. (2020). Development and validation of prediction model for risk reduction of metabolic syndrome by body weight control: A prospective population-based study. Scientific Reports, 10(1), 10006. https://doi.org/10.1038/s41598-020-67238-5
Lee, S., Lee, S. K., Kim, J. Y., Cho, N., & Shin, C. (2017). Sasang constitutional types for the risk prediction of metabolic syndrome: A 14-year longitudinal prospective cohort study. BMC Complementary and Alternative Medicine, 17(1), 438. https://doi.org/10.1186/s12906-017-1936-4
Lejsková, M., Alušík, S., Suchánek, M., Zecová, S., & Piha, J. (2011). Menopause: Clustering of metabolic syndrome components and population changes in insulin resistance. Climacteric, 13(1), 83-91. https://doi.org/10.3109/13697131003692745
Li, G., Esangbedo, I. C., Xu, L., Fu, J., Li, L., Feng, D., Han, L., Xiao, X., Li, M., Mi, J., Li, M., Gao, S., & Hou, Y. (2018). Childhood retinol-binding protein 4 (RBP4) levels predicting the 10-year risk of insulin resistance and metabolic syndrome: The BCAMS study. Cardiovascular Diabetology, 17(1), 69. https://pubmed.ncbi.nlm.nih.gov/29759068/
Mesch, V. R., Boero, L. E., Siseles, N. O., Royer, M., Prada, M., Sayegh, F., Schreier, L., Benencia, H. J., & Berg, G. A. (2006). Metabolic syndrome throughout the menopausal transition: Influence of age and menopausal status. Climacteric, 9(1), 40-48. https://doi.org/10.1080/13697130500487331
Mesch, V. R., Siseles, N. O., Maidana, P. N., Boero, L. E., Sayegh, F., Prada, M., Royer, M., Schreier, L., Benencia, H. J., & Berg, G. A. (2008). Androgens in relationship to cardiovascular risk factors in the menopausal transition. Climacteric, 11(6), 509-517. https://doi.org/10.1080/13697130802416640
Miche, M., Studerus, E., Meyer, A. H., Gloster, A. T., Beesdo-Baum, K., Wittchen, H. U., & Lieb, R. (2020). Prospective prediction of suicide attempts in community adolescents and young adults, using regression methods and machine learning. Journal of Affective Disorders, 265, 570-578. https://doi.org/10.1016/j.jad.2019.11.093
Molnar, C., König, G., Herbinger, J., Freiesleben, T., Dandl, S., Scholbeck, C. A., Casalicchio, G., Grosse-Wentrup, M., & Bischl, B. (2022). General pitfalls of model-agnostic interpretation methods for machine learning models. In Proceedings of the xxAI-Beyond Explainable AI: International Workshop, Held in Conjunction with ICML 2020 (pp. 39-68). Springer. General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models | SpringerLink
Motamed, N., Perumal, D., Zamani, F., Ashrafi, H., Haghjoo, M., Saeedian, F. S., Maadi, M., Akhavan-Niaki, H., Rabiee, B., & Asouri, M. (2015). Conicity index and waist-to-hip ratio are superior obesity indices in predicting 10-year cardiovascular risk among men and women. Clinical Cardiology, 38(9), 527-534. https://doi.org/10.1002/clc.22437
O'Keefe, E. L., DiNicolantonio, J. J., Patil, H., Helzberg, J. H., & Lavie, C. J. (2016). Lifestyle choices fuel epidemics of diabetes and cardiovascular disease among Asian Indians. Progress in Cardiovascular Diseases, 58(5), 505-513. https://doi.org/10.1016/j.pcad.2015.08.010
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How to Cite
Ghosh, J., Roy Chaudhury, S., Singh, K. and Koner, S. (2025) “Development and Performance Analysis of Machine Learning Methods for Predicting Metabolic Syndrome Among Postmenopausal Women of India”, International Journal of Advancement in Life Sciences Research, 8(1), pp. 62-75. doi: https://doi.org/10.31632/ijalsr.2025.v08i01.006.
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