ArticleBMC medical informatics and decision making2026
Machine learning-based prediction model for predicting the impact of insulin resistance on the risk of ischemic cardiomyopathy.
Article in BMC medical informatics and decision making, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundThe triglyceride-to-high-density lipoprotein cholesterol (TG/HDL-C) ratio and triglyceride glucose-body mass (TyG-BMI) index are reliable indicators of insulin resistance (IR). This study investigated their association with ischemic cardiomyopathy (ICM) and developed a machine learning-based model for ICM risk prediction.
methodsIn total, 1,603 subjects participated in this study. Univariable logistic regression analysis was conducted, and variables with P < 0.05 were selected for multivariable logistic regression to identify independent risk factors for ICM. Variables meeting this criterion were adopted to create eight machine learning models, from which the optimal model was selected. Using this best-performing model, SHAP values were visualized, and an online calculator was developed. The model was validated via a calibration plot and DCA.
resultsUnivariate and multivariate logistic regression analyses revealed that TyG-BMI, age, ejection fraction, TC/HDL-C, sex, HDL-C, TC, BMI, hemoglobin, diabetes, and hypertension were independent risk factors for ICM (P < 0.05). Based on these factors, SHAP visualization and an online calculator were developed. The calibration plot indicated strong alignment between the model's predicted and actual values, whereas the DCA demonstrated the model's clinical utility.
conclusionThe TyG-BMI and TC/HDL-C ratio independently predict ICM risk, with the XGB model identified as the most effective for ICM risk prediction, indicating substantial clinical applicability. CLINICAL TRIAL REGISTRATION NUMBER: Not applicable.
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