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However, more evidence is required to establish its clinical impact and explanatory power compared to other diagnostic variables. In this study, random forest classification was used on data from 195 subjects to discriminate between healthy vs. obese non\u2010diabetic, healthy vs. diabetic, healthy vs. non\u2010obese diabetic, obese non\u2010diabetic vs. obese diabetic, and non\u2010obese diabetic vs. obese diabetic patient subgroups using P\u2010MRS and H\u2010MRS measurements of cardiac energetics, along with MRI measures of cardiac function, fat volumes, blood pressure, and blood glucose and cholesterol levels. Achieving 78.12%, 88.57%, 85.19%, 90.48%, and 76.47% test accuracies, SHAPs (SHapley Additive exPlanations) feature importances indicate a higher predictive impact of metabolic metrics for classifying the diabetic heart compared to global function metrics, gained through most common imaging techniques. Bayesian networks generated through structure learning of the data further suggests a potential causal association of increased visceral fat, increased LVMass resulting in decreased PCr\/ATP, and increased cardiac lipid levels attributed to these disease states. Through the results, we have been able to showcase the importance of MRS measurements, both as strong features separating the diseases with clinically\u2010relevant accuracy and furthermore its causal connection to other cardiac parameters.<\/jats:p>","DOI":"10.1002\/nbm.70400","type":"journal-article","created":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T05:13:13Z","timestamp":1789189993000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MR Derived Cardiac Metabolism Changes in Patients With Obesity and Diabetes: Knowledge Discovery Via Bayesian Networks and Random Forest Classification"],"prefix":"10.1002","volume":"39","author":[{"given":"Ina","family":"Hanninger","sequence":"first","affiliation":[{"name":"Institute of Biomedical Engineering University of Oxford  Oxford UK"},{"name":"Oxford Centre for Clinical Magnetic Resonance Research, RDM Cardiovascular Medicine University of Oxford  Oxford 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