Machine Learning Models for Metabolic Syndrome Identification with Explainable AI
DOI:
https://doi.org/10.52436/1.jutif.2025.6.3.4430Keywords:
Explainable AI, K-Means, Hypertension, Machine Learning, Metabolic Syndrome, XGBoostAbstract
Metabolic syndrome (MetS) is a cluster of interrelated risk factors, including hypertension, dyslipidemia, central obesity, and insulin resistance, significantly increasing the likelihood of cardiovascular diseases and type 2 diabetes. Early identification of hypertension, a key component of MetS, is essential for timely intervention and effective disease management. This research aims to develop a hybrid machine learning model that integrates XGBoost classification with K-Means clustering to enhance or strengthening of hypertension prediction and identify distinct patient subgroups based on metabolic risk factors. The dataset consists of 1,878 patient records with metabolic parameters such as systolic and diastolic blood pressure, fasting glucose, cholesterol levels, and anthropometric measurements. Model performance was assessed using accuracy, precision, recall, F1-score, and ROC-AUC. The proposed XGBoost model achieved an outstanding classification performance with 98% accuracy, 98% precision, 98% recall, 98% F1-score, and an ROC-AUC of 1.00. K-Means clustering further identified five distinct patient subgroups with varying metabolic risk profiles. The findings underscore the potential of machine learning-driven decision support systems in improving hypertension diagnosis and MetS management.
Downloads
References
M. G. Sánchez-Otero, A. Alexander-Aguilera, A. Ramírez-Higuera, M. Juárez-Arias, and R. M. Oliart-Ros, “The metabolic syndrome,” in Phytochemicals from Mexican Medicinal Plants: Potential Biopharmaceuticals against Noncommunicable Diseases, 2020. DOI: 10.1177/0884533609342436
Whelton PK, Carey RM, Aronow WS, Casery DE, Collins KJ, and Himmelfarb CD, “ACC/AHA/AAPA/ABC/ACPM/AGS/ APhA/ ASH/ ASPC/ NMA / PCNA Guideline for the Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults,” Hypertension, 2018. 71(19) e13-e115
D. Basu, R. Sinha, S. Sahu, J. Malla, N. Chakravorty, and P. S. Ghosal, “Identification of severity and passive measurement of oxidative stress biomarkers for β–thalassemia patients: K-means, random forest, XGBoost, decision tree, neural network based novel framework,” Adv. Redox Res., 2022. DOI: 10.1016/j.arres.2022.100034
S. S. Negi, M. Memoria, R. Kumar, K. Joshi, S. D. Pandey, and A. Gupta, “Machine Learning Based Hybrid Technique for Heart Disease Prediction,” in 2022 International Conference on Advances in Computing, Communication and Materials, ICACCM 2022, 2022. DOI: 10.1109/ICACCM56405.2022.10009219
G. Marchesini et al., “Nonalcoholic fatty liver, steatohepatitis, and the metabolic syndrome,” Hepatology, 2003. DOI: 10.1053/jhep.2003.50161
K. Alberti et al., “Harmonizing the Metabolic Syndrome A Joint Interim Statement of the International Diabetes Federation Task Force on Epidemiology and Prevention for the Study of Obesity,” Circulation, 2009. 120 1640 - 1645
M. G. Saklayen, “The Global Epidemic of the Metabolic Syndrome,” Current Hypertension Reports. 2018. DOI: 10.1007/s11906-018-0812-z
S. Mottillo et al., “The metabolic syndrome and cardiovascular risk: A systematic review and meta-analysis,” J. Am. Coll. Cardiol., 2010.
G. M. Reaven, “The metabolic syndrome: Requiescat in Pace,” Clinical Chemistry. 2005. DOI: 10.1373/clinchem.2005.048611
B. M. Egan, “Insulin resistance and the sympathetic nervous system,” Current Hypertension Reports. 2003. DOI: 10.1007/s11906-003-0028-7
M. C. S. Moreira et al., “Does the sympathetic nervous system contribute to the pathophysiology of metabolic syndrome?,” Frontiers in Physiology. 2015. DOI: 10.3389/fphys.2015.00234
Yeni Yulianti, Teten Tresnawan, Yosep Purnairawan, Susilawati, and Arneta Oktavia, “Identification Of Factors Affecting The Quality Of Life In Hypertension Patients,” Healthc. Nurs. J., 2023. DOI: 10.35568/healthcare.v5i2.3307
S. Ghosh and S. Kumar, “Comparative Analysis of K-Means and Fuzzy C-Means Algorithms,” Int. J. Adv. Comput. Sci. Appl., 2013. DOI: 10.14569/ijacsa.2013.040406
A. M. Ikotun, A. E. Ezugwu, L. Abualigah, B. Abuhaija, and J. Heming, “K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big data,” Inf. Sci. (Ny)., 2023. DOI: 10.1016/j.ins.2022.11.139
M. Moradi Fard, T. Thonet, and E. Gaussier, “Deep k-Means: Jointly clustering with k-Means and learning representations,” Pattern Recognit. Lett., 2020. DOI: 10.1016/j.patrec.2020.07.028
K. P. Sinaga and M. S. Yang, “Unsupervised K-means clustering algorithm,” IEEE Access, 2020. DOI: 10.1109/ACCESS.2020.2988796
T. Chen and C. Guestrin, “XGBoost: A scalable tree boosting system,” in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016. DOI: 10.1145/2939672.2939785
M. F. Ijaz, G. Alfian, M. Syafrudin, and J. Rhee, “Hybrid Prediction Model for type 2 diabetes and hypertension using DBSCAN-based outlier detection, Synthetic Minority Over Sampling Technique (SMOTE), and random forest,” Appl. Sci., 2018. DOI: 10.3390/app8081325
P. Dhar, K. Suganya Devi, S. K. Satti, and P. Srinivasan, “An hybrid soft attention based XGBoost model for classification of poikilocytosis blood cells,” Evol. Syst., 2024. DOI: 10.1007/s12530-023-09549-2
S. M. Lundberg and S. I. Lee, “A unified approach to interpreting model predictions,” in Advances in Neural Information Processing Systems, 2017. December 4766-4775
L. Li, Q. Song, and X. Yang, “K-means clustering of overweight and obese population using quantile-transformed metabolic data,” Diabetes, Metab. Syndr. Obes., 2019. DOI: 10.2147/DMSO.S206640
T. Do et al., “Utilizing XGBoost for the Prediction of Material Corrosion Rates from Embedded Tabular Data using Large Language Model,” in Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023, 2023. DOI: 10.1109/BIBM58861.2023.10385544
V. P. Kovacheva, B. W. Eberhard, R. Y. Cohen, M. Maher, R. Saxena, and K. J. Gray, “Preeclampsia Prediction Using Machine Learning and Polygenic Risk Scores from Clinical and Genetic Risk Factors in Early and Late Pregnancies,” Hypertension, 2024. DOI: 10.1161/HYPERTENSIONAHA.123.21053
M. Ridley, “Explainable Artificial Intelligence (XAI),” Inf. Technol. Libr., 2022. DOI: 10.6017/ITAL.V41I2.14683
S. Sakr et al., “Using Machine Learning on Cardiorespiratory Fitness Data for Predicting Hypertension: The Henry Ford ExercIse Testing (FIT) Project,” PLoS One, 2018. DOI: 10.1371/journal.pone.0195344
V. Kublanov, A. Dolganov, D. Belo, and H. Gambôa, “Comparison of Machine Learning Methods for the Arterial Hypertension Diagnostics,” Appl. Bionics Biomech., 2017. DOI: 10.1155/2017/5985479
N. Nuryani, T. P. Utomo, N. Wiyono, A. D. Sutomo, and S. H. Ling, “Cuffless Hypertension Detection Using Swarm Support Vector Machine Utilizing Photoplethysmogram and Electrocardiogram,” J. Biomed. Phys. Eng., 2023. DOI: 10.31661/jbpe.v0i0.2206-1504
F. Di Martino and F. Delmastro, “Explainable AI for clinical and remote health applications: a survey on tabular and time series data,” Artif. Intell. Rev., 2023. DOI: 10.1007/s10462-022-10304-3
M. Ahmed, R. Seraj, and S. M. S. Islam, “The k-means algorithm: A comprehensive survey and performance evaluation,” Electronics (Switzerland). 2020. DOI 10.3390/electronics9081295
M. Fang et al., “A hybrid machine learning approach for hypertension risk prediction,” Neural Comput. Appl., 2023. DOI: 10.1007/s00521-021-06060-0
Additional Files
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Egga Asoka, Egga Asoka, Fathoni, Anggina Primanita, Indra Griha Tofik Isa
This work is licensed under a Creative Commons Attribution 4.0 International License.
