{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T11:08:50Z","timestamp":1790075330453,"version":"4.0.1"},"reference-count":38,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T00:00:00Z","timestamp":1790035200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background: Metabolic syndrome (MetS) is a complex health problem significantly associated with cardiovascular diseases and type 2 diabetes mellitus. Traditional diagnostic approaches rely on invasive biochemical markers, which limit their accessibility. Here, we developed an explainable machine learning (ML) framework for MetS prediction based on non-invasive demographic, anthropometric, and behavioral variables. Method: We conducted a retrospective observational study that included 1090 participants with MetS and 584 without MetS, using six supervised ML models, including Random Forest, AdaBoost, K-Nearest Neighbors, Bagging, Logistic Regression, and Ensemble learning. We trained and validated these models using stratified 10-fold cross-validation. Predictive performance was evaluated using accuracy, precision, recall, specificity, negative predictive value, area under the receiver operating characteristic curve (AUC) and F1-score. Shapley Additive Explanations (SHAP) applied to the Random Forest classifier evaluated interpretability. A 1:1 balanced sensitivity analysis was subsequently performed using the same leakage-safe predictor set and validation framework to assess whether model performance was materially influenced by outcome prevalence. Results: In the natural-prevalence primary cohort (n = 1674; 65.1% with MetS), leakage-safe models showed moderate discrimination. Ensemble achieved the highest mean AUC (0.751, 95% CI 0.731\u20130.771), followed by Random Forest (0.745, 95% CI 0.723\u20130.767) and Bagging (0.742, 95% CI 0.725\u20130.760). SHAP analysis identified (body mass index) BMI, age, and body weight as the dominant contributors, with additional contributions from physical activity, and dietary and sociodemographic variables. In the 1:1 balanced sensitivity cohort (n = 1000), discrimination remained similar, with Ensemble achieving the highest mean AUC (0.742, 95% CI 0.704\u20130.780). Conclusion: Explainable ML models based on accessible non-laboratory variables provided moderate discrimination of prevalent MetS and may serve as candidate pre-laboratory risk-stratification tools. They should not replace established diagnostic criteria, and external validation, recalibration, and prospective evaluation of clinically relevant thresholds are required before implementation.<\/jats:p>","DOI":"10.3390\/diagnostics16193073","type":"journal-article","created":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T10:21:48Z","timestamp":1790072508000},"page":"3073","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Non-Invasive Prediction of Metabolic Syndrome Using Explainable Machine Learning"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-2987-458X","authenticated-orcid":false,"given":"Islam A.","family":"Berdaweel","sequence":"first","affiliation":[{"name":"Department of Clinical Pharmacy and Pharmacy Practice, Faculty of Pharmacy, Yarmouk University, Irbid 21163, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sayer I.","family":"Al-Azzam","sequence":"additional","affiliation":[{"name":"Department of Clinical Pharmacy, Faculty of Pharmacy, Jordan University of Science and Technology, Irbid 22110, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ghaith","family":"Al-Taani","sequence":"additional","affiliation":[{"name":"Department of Clinical Pharmacy and Pharmacy Practice, Faculty of Pharmacy, Yarmouk University, Irbid 21163, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-9585-6577","authenticated-orcid":false,"given":"Amal","family":"Albawaana","sequence":"additional","affiliation":[{"name":"Faculty of Pharmacy, Department of Medicinal Chemistry and Pharmacognosy, Yarmouk University, Irbid 21163, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4338-2163","authenticated-orcid":false,"given":"Salam","family":"Shannag","sequence":"additional","affiliation":[{"name":"Department of Clinical Pharmacy and Pharmacy Practice, Faculty of Pharmacy, Yarmouk University, Irbid 21163, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"given":"Ameera M.","family":"Bataineh","sequence":"additional","affiliation":[{"name":"Independent Researcher, Irbid 21110, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5020-6838","authenticated-orcid":false,"given":"Sireen Abdul Rahim","family":"Shilbayeh","sequence":"additional","affiliation":[{"name":"Department of Pharmacy Practice, Pharmacy college, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"resources","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5190-3048","authenticated-orcid":false,"given":"Amjed","family":"Al Fahoum","sequence":"additional","affiliation":[{"name":"Biomedical Systems and Informatics Engineering Department, Hijjawi Faculty for Engineering Technology, Yarmouk University, Irbid 21163, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Gruber, J., Santos, B.S.d., Mar\u00e7\u00e3o, M., Durdana, I., Lee, B., Vumma, L., Segato-Dezem, F., Chasteen, H., Zhang, S., and Lewis, C. (2026). GLP-1 Targeting Agents Impair Chemoimmunotherapy Effectiveness in Triple-Negative Breast Cancer. Res. 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