Identifying Metabolic Syndrome Easily and Cost Effectively Using Non-Invasive Methods with Machine Learning Models
Open accessAbstract
Purpose: The objective of this study was to employ machine learning (ML) models utilizing non-invasive factors to achieve early and low-cost identification of MetS in a large physical examination population. Patients and Methods: The study enrolled 9171 participants who underwent physical examinations at Northern Jiangsu People's Hospital in 2009 and 2019, to determine MetS based on criteria established by the Chinese Diabetes Society. Non-invasive characteristics such as gender, age, body mass index (BMI), systolic blood pressure (SBP), and diastolic blood pressure (DBP) were collected and used as input variables to train and evaluate ML models for MetS identification. Several ML models were used for MetS identification, including logistic regression (LR), k-nearest neighbors algorithm (k-NN), naive bayesian (NB), decision tree (DT), random forest (RF), artificial neural network (ANN), and support vector machine (SVM). Results: Our ML models all showed good performance in the 10-fold cross-validation except for the SVM model. In the external validation, the NB model exhibited the best performance with an AUC of 0.976, accuracy of 0.923, sensitivity of 98.32%, and specificity of 91.32%. Conclusion: This study proposed a new non-invasive method for early and low-cost identification of MetS by using ML models. This approach has the potential to serve as a highly sensitive, convenient, and cost-effective tool for large-scale MetS screening.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Machine learning has demonstrated effectiveness in the early detection and cost-effective screening of metabolic syndrome.
“Notably, ML has demonstrated its effectiveness in the early detection and cost-effective screening of MetS [11].”
From Machine learning with the body roundness index and associated indicators: a new approach to predicting metabolic syndrome (2025) · cites this paper as [11]
The Discussion reports strong detection performance for an ML model and describes its use for early screening. The Abstract Conclusion calls it a method for early, low-cost identification, but says it has the *potential* to be a cost-effective screening tool; it does not establish cost-effective screening as a demonstrated outcome. (LLM, conf=0.83)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Toprak, Ö., Kurt, H., Sarı, Y., Şarkış, C., Us, H., & Kırık, A. (2017) · Kidney & Blood Pressure Research · doi:10.1159/000468530 · Cited by 29,769 · see citing works
Expert Panel on Detection, E. (2001) · JAMA: The Journal of the American Medical Association · doi:10.1001/jama.285.19.2486 · Cited by 26,011 · see citing works
A logical calculus of the ideas immanent in nervous activity
McCulloch, W. S., & Pitts, W. (1943) · The Bulletin of Mathematical Biophysics · doi:10.1007/bf02478259 · Cited by 18,566 · see citing works
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362 · see citing works
Grundy, S. M., Cleeman, J. I., Daniels, S. R., Donato, K. A., Eckel, R. H., Franklin, B. A., Gordon, D. J., Krauss, R. M., Savage, P. J., Smith, S. C., et al. (2005) · Circulation · doi:10.1161/circulationaha.105.169404 · Cited by 11,961 · see citing works
The metabolic syndrome—a new worldwide definition
Alberti, K. G. M., Zimmet, P., & Shaw, J. (2005) · The Lancet · doi:10.1016/s0140-6736(05)67402-8 · Cited by 9,352 · see citing works
Ford, E. S., Giles, W. H., & Dietz, W. H. (2002) · JAMA · doi:10.1001/jama.287.3.356 · Cited by 6,698 · see citing works
The Global Epidemic of the Metabolic Syndrome
Saklayen, M. G. (2018) · Current Hypertension Reports · doi:10.1007/s11906-018-0812-z · Cited by 3,990 · see citing works
The Metabolic Syndrome and Cardiovascular Risk
Mottillo, S., Filion, K. B., Genest, J., Joseph, L., Pilote, L., Poirier, P., Rinfret, S., Schiffrin, E. L., & Eisenberg, M. J. (2010) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2010.05.034 · Cited by 3,009 · see citing works
Cornier, M.-A., Dabelea, D., Hernandez, T. L., Lindstrom, R. C., Steig, A. J., Stob, N. R., Van Pelt, R. E., Wang, H., & Eckel, R. H. (2008) · Endocrine Reviews · doi:10.1210/er.2008-0024 · Cited by 2,162 · see citing works
Comment on the provisional report from the WHO consultation
(1999) · Diabetic Medicine · doi:10.1046/j.1464-5491.1999.00059.x · Cited by 1,820 · see citing works
Tu, J. V. (1996) · Journal of Clinical Epidemiology · doi:10.1016/s0895-4356(96)00002-9 · Cited by 1,818 · see citing works
Lakhani, P., & Sundaram, B. (2017) · Radiology · doi:10.1148/radiol.2017162326 · Cited by 1,775 · see citing works
Trends in the Prevalence of Metabolic Syndrome in the United States, 2011-2016
Hirode, G., & Wong, R. J. (2020) · JAMA · doi:10.1001/jama.2020.4501 · Cited by 936 · see citing works
A logical calculus of the ideas immanent in nervous activity
McCulloch, W. S., & Pitts, W. (1990) · Bulletin of Mathematical Biology · doi:10.1016/s0092-8240(05)80006-0 · Cited by 926 · see citing works
Prevalence of the metabolic syndrome and overweight among adults in China
Gu, D., Reynolds, K., Wu, X., Chen, J., Duan, X., Reynolds, R. F., Whelton, P. K., & He, J. (2005) · The Lancet · doi:10.1016/s0140-6736(05)66375-1 · Cited by 917 · see citing works
Terrin, N. C., Schmid, C. H., Griffith, J. L., D’Agostino, R. B., & Selker, H. P. (2003) · Journal of Clinical Epidemiology · doi:10.1016/s0895-4356(03)00120-3 · Cited by 134 · see citing works
The metabolic syndrome and chronic kidney disease in a Southeast Asian cohort.
Kitiyakara, C., Yamwong, S., Cheepudomwit, S., Domrongkitchaiporn, S., Unkurapinun, N., Pakpeankitvatana, V., & Sritara, P. (2007) · Kidney International · doi:10.1038/sj.ki.5002128 · Cited by 113 · see citing works
de Edelenyi, F. S., Goumidi, L., Bertrais, S., Phillips, C. M., MacManus, R., Roche, H. M., Planells, R., & Lairon, D. (2008) · Genes & Nutrition · doi:10.1007/s12263-008-0097-y · Cited by 79 · see citing works
Zhang, L., Gonçalves, C. M. V., Dai, L., Lü, H., Huang, J., Ji, H., Wang, D., Yi, L., & Liang, Y. (2014) · Analytica Chimica Acta · doi:10.1016/j.aca.2014.04.008 · Cited by 76 · see citing works
Bone Tumor Diagnosis Using a Naïve Bayesian Model of Demographic and Radiographic Features
Do, B. H., Langlotz, C. P., & Beaulieu, C. F. (2017) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-017-0001-7 · Cited by 75 · see citing works
Hirose, H., Takayama, T., Hozawa, S., Hibi, T., & Saito, I. (2011) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2011.09.005 · Cited by 72 · see citing works
Identification of metabolic syndrome using decision tree analysis
Worachartcheewan, A., Nantasenamat, C., Isarankura‐Na‐Ayudhya, C., Pidetcha, P., & Prachayasittikul, V. (2010) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2010.06.009 · Cited by 61 · see citing works
Predicting Metabolic Syndrome Using the Random Forest Method
Worachartcheewan, A., Shoombuatong, W., Pidetcha, P., Nopnithipat, W., Prachayasittikul, V., & Nantasenamat, C. (2015) · The Scientific World JOURNAL · doi:10.1155/2015/581501 · Cited by 58 · see citing works
Predicting metabolic syndrome using decision tree and support vector machine methods.
Karimi-Alavijeh, F., Jalili, S., & Sadeghi, M. (2016) · Europe PMC (PubMed Central) · Cited by 56 · see citing works
Chen, B., Yang, D., Chen, Y., Xu, W., Ye, B., & Ni, Z. (2010) · Clinica Chimica Acta · doi:10.1016/j.cca.2010.01.038 · Cited by 55 · see citing works
Self-citationLin, C.-C., Bai, Y., Chen, J.-Y., Hwang, T., Chen, T.-T., Chiu, H., & Li, Y. (2009) · The Journal of Clinical Psychiatry · doi:10.4088/jcp.08m04628yel · Cited by 45 · see citing works
Risk models and scores for metabolic syndrome: systematic review protocol
Ibrahim, M. S., Pang, D., Randhawa, G., & Pappas, Y. (2019) · BMJ Open · doi:10.1136/bmjopen-2018-027326 · Cited by 34 · see citing works
Wang, H., Wang, Y., Li, X., Deng, X., Kong, Y., Wang, W., & Zhou, Y. (2022) · Cardiovascular Diabetology · doi:10.1186/s12933-022-01716-0 · Cited by 34 · see citing works
Cefalu, W. T., & Rodgers, G. P. (2021) · Cell Metabolism · doi:10.1016/j.cmet.2021.02.006 · Cited by 30 · see citing works
Worachartcheewan, A., Nantasenamat, C., Isarankura‐Na‐Ayudhya, C., & Prachayasittikul, V. (2013) · Chemical Papers · doi:10.2478/s11696-013-0398-5 · Cited by 29 · see citing works
Quantitative population-health relationship (QPHR) for assessing metabolic syndrome
Worachartcheewan, A., Nantasenamat, C., Isarankura‐Na‐Ayudhya, C., & Prachayasittikul, V. (2013) · EXCLI journal · doi:10.17877/de290r-7342 · Cited by 21 · see citing works
Data mining for the identification of metabolic syndrome status
Worachartcheewan, A., Schaduangrat, N., Prachayasittikul, V., & Nantasenamat, C. (2018) · EXCLI journal · doi:10.17179/excli2017-911 · Cited by 20 · see citing works
Pang, C., Jia, L., Hou, X., Gao, X., Liu, W., Bao, Y., & Jia, W. (2014) · PLoS ONE · doi:10.1371/journal.pone.0097928 · Cited by 20 · see citing works
Hsu, N., Chou, K.-C., Wang, Y.-T. T., Hung, C., Kuo, C.-F., & Tsai, S. (2022) · Journal of Translational Medicine · doi:10.1186/s12967-022-03379-7 · Cited by 18 · see citing works
Kim, T. N., Kim, J., Won, J. C., Park, M. S., Lee, S., Yoon, S. H., Kim, H.-R., Ko, K. S., & Rhee, B. D. (2012) · PubMed · doi:10.3275/8235 · Cited by 17 · see citing works
Methods for algorithmic diagnosis of metabolic syndrome
Vrbaški, D., Vrbaški, M., Kupusinac, A., Ivanović, D., Stokić, E., Ivetić, D., & Doroslovački, K. (2019) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2019.101708 · Cited by 16 · see citing works
Ushida, Y., Kato, R., Niwa, K., Tanimura, D., Izawa, H., Yasui, K., Takase, T., Yoshida, Y., Kawase, M., Yoshida, T., et al. (2012) · BMC Medical Informatics and Decision Making · doi:10.1186/1472-6947-12-80 · Cited by 16 · see citing works
Discovery of association rules in Metabolic Syndrome related diseases
Chan, C., Chen, C., & Liu, B. (2008) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2008.4633898 · Cited by 13 · see citing works
Wang, F.-H., & Lin, C. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17249288 · Cited by 12 · see citing works
Huang, Y.-C. (2013) · Health Information Management Journal · doi:10.1177/183335831304200304 · Cited by 11 · see citing works
Diagnosing Metabolic Syndrome Using Genetically Optimised Bayesian ARTMAP
Kakudi, H. A., Loo, C. K., Moy, F. M., Masuyama, N., & Pasupa, K. (2018) · IEEE Access · doi:10.1109/access.2018.2880224 · Cited by 4 · see citing works
Alberti (1998) · doi:10.1002/(sici)1096-9136(199807)15:7
Flagged first, then by citations
Research works printed but not linked: 1
- [26]Shamrat FM, Chakraborty S, Billah MM, Das P, Ranjan R. A comprehensive study on pre-pruning and post- pruning methods of decision tree classification algorithm. In: 5th International Conference on Trends in Electronics and Informatics (ICOEI 2021); 2021. Available from: http://ieeexplore.ieee.org/document/9452898/.
Cited by
Sorted by citations
Metabolic syndrome predictive modelling in Bangladesh applying machine learning approach
Hossain, M. F., Hossain, S., Akter, M. N., Nahar, A., Liu, B., & Faruque, M. O. (2024) · PLoS ONE · doi:10.1371/journal.pone.0309869 · Cited by 7 · see citing works
He, Y., Chen, Z., Tang, Z., Qin, Y., & Wang, F. (2025) · BMC Public Health · doi:10.1186/s12889-025-23858-y · Cited by 5 · see citing works
Wang, Y., Li, Y., Jiao, S., Pan, Y., Deng, X., Qin, Y., Zhao, D., & Liu, Z. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e35065 · Cited by 5 · see citing works
Esmailzadeh, A., Norouzkhani, N., Dokhtfaroughi, S. S., Rasoulian, A., & Habibi, M. R. M. (2026) · Health Science Reports · doi:10.1002/hsr2.72636 · Cited by 2 · see citing works
Correlation between salivary uric acid values and risk factors associated with metabolic syndrome
Anastasovska, M., Poposki, B., Andonovska, M., Idoska, S., Idoski, E., & Kokolanski, V. (2025) · Archives of Public Health · doi:10.3889/aph.2025.6573 · Cited by 2 · see citing works
Farboud, M., Hessam, S., Vahdat, S., & Tofighi, S. (2026) · BMC Research Notes · doi:10.1186/s13104-026-07859-7 · Cited by 1 · see citing works
Enhancing Impaired Waist-to-Height Ratio Classification Using Neural Networks
Severeyn, É., Cruz, A. L., & Huerta, M. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-46739-4_20
Etoom, M., & Ahmad, M. M. (2026) · Network Modeling Analysis in Health Informatics and Bioinformatics · doi:10.1007/s13721-026-00869-2
Non-Invasive Prediction of Metabolic Syndrome Using Explainable Machine Learning
Berdaweel, I. A., Al-Azzam, S. I., Al‐Taani, G. M., Albawaana, A. S., Shannag, S., Bataineh, A. M., Shilbayeh, S. A. R., & Fahoum, A. S. A. (2026) · Diagnostics · doi:10.3390/diagnostics16193073
- Authors
- Xu, W., Zhang, Z., Hu, K., Fang, P., Li, R., Kong, D., Xuan, M., Yue, Y., She, D., & Xue, Y.
- Affiliations
- Tongji University (CN)
- Northern Jiangsu People's Hospital (CN)
- Corresponding author
- She, Dunmin
- Xue, Ying
- Venue
- Diabetes, Metabolic Syndrome and Obesity
- Publisher
- Informa UK Limited
- Published
- 2023-07
- Volume
- Volume 16
- Pages
- 2141-2151
- ISSN
- 1178-7007 (ISSN-L)
- PMC
- PMC10361460
- OpenAlex
- W4384562195
- Cited by
- 9
- References
- 44
- Citations by year
- 2026: 4 · 2025: 2 · 2024: 2 · 2023: 1
- FWCI
- 2.02
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Diabetes Metabolic Syndrome and Obesity (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Dove Medical Press (Taylor and Francis Group) (submittedVersion, cc-by-nc)
- Funders
- Natural Science Foundation of Shanghai
- National Natural Science Foundation of China (81974105)
- Tongji University
- Fundamental Research Funds for the Central Universities
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Keywords
- metabolic syndrome, non-invasive screening, machine learning classification, body mass index, blood pressure, early detection, cost-effective screening, Naive Bayes classifier, physical examination population
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
