BMC medical informatics and decision making · 2026
Machine learning-based prediction model for predicting the impact of insulin resistance on the risk of ischemic cardiomyopathy.
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Naman Tuersunjiang, Cheng Hui, Yu Xiao-Lin, Guo Zi-Tong
Аннотация
BACKGROUND: The 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.
METHODS: In 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.
RESULTS: Univariate 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.
CONCLUSION: The 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.
METHODS: In 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.
RESULTS: Univariate 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.
CONCLUSION: The 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.