In this study, Random Forest classification was used on data from 197 subjects to discriminate between non-diabetic, diabetic, and obese patients using 31P-MRS and 1H-MRS measurements of cardiac energetics, along with MRI measures of cardiac function. Achieving 91.67%, 73.08% and 88.89% test accuracies, SHAP feature importances indicate a higher predictive impact of metabolic metrics for classifying the diabetic heart compared to global function metrics. 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.
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