Reviews in cardiovascular medicine · 2026
Identification and Validation of an Explainable Predictive Model For Heart Failure in Patients With Hypertension.
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Han Jiayi, Zhao Tengxiao, Shi Yuncong, Zhao Zehao, Wang Wenjie, Ye Yi, Bai Yingxuan, Lin Zhihan, Meng Xiangfei, Guo Liwei, Feng Ruixiang, Ding Yaodong, Zeng Yong
Аннотация
BACKGROUND: Heart failure (HF) is a heterogeneous syndrome affecting over 60 million individuals globally. Patients with hypertension are particularly susceptible to developing HF. Therefore, timely identification and predictive assessment of HF risk have significant clinical implications in this population. Thus, this study aimed to develop a new interpretable machine learning (ML) model for HF prediction.
METHODS: Using data from the Systolic Blood Pressure Intervention Trial (SPRINT), a random under-sampling technique was applied to address class imbalance in the target variable, achieving a 1:1 ratio between positive and negative samples. By randomly matching 162 individuals without HF events to those with events, a balanced dataset comprising 324 participants was constructed. The test set comprised 40% of the total dataset to ensure a robust evaluation of model performance. Seven ML algorithms, including support vector machine (SVM), adaptive boosting (Adaboost), naïve Bayes (NB), logistic regression (LR), gradient boosting machine (GBM), random forest (RF), and multilayer perceptron (MLP), were employed to construct the predictive models. Model performance was evaluated using the area under the curve (AUC), decision curve analysis (DCA), calibration curves, and other metrics. The SHapley Additive exPlanations (SHAP) approach was employed to rank feature significance and provide interpretability for the final model.
RESULTS: Over a median follow-up of 3.88years, 162 patients (1.8%) developed incident HF. Among the seven ML models, GBM demonstrated the best performance. A total of 14 features were retained after the least absolute shrinkage and selection operator (LASSO) selection. The final model exhibited robust predictive capability for identifying HF risk, with an overall accuracy of 0.731, a precision of 0.770, and an AUC (95% confidence interval (CI)) of 0.763 (0.676-0.840).
CONCLUSION: The GBM-based explainable prediction model demonstrated robust performance in predicting HF risk among patients with hypertension.
METHODS: Using data from the Systolic Blood Pressure Intervention Trial (SPRINT), a random under-sampling technique was applied to address class imbalance in the target variable, achieving a 1:1 ratio between positive and negative samples. By randomly matching 162 individuals without HF events to those with events, a balanced dataset comprising 324 participants was constructed. The test set comprised 40% of the total dataset to ensure a robust evaluation of model performance. Seven ML algorithms, including support vector machine (SVM), adaptive boosting (Adaboost), naïve Bayes (NB), logistic regression (LR), gradient boosting machine (GBM), random forest (RF), and multilayer perceptron (MLP), were employed to construct the predictive models. Model performance was evaluated using the area under the curve (AUC), decision curve analysis (DCA), calibration curves, and other metrics. The SHapley Additive exPlanations (SHAP) approach was employed to rank feature significance and provide interpretability for the final model.
RESULTS: Over a median follow-up of 3.88years, 162 patients (1.8%) developed incident HF. Among the seven ML models, GBM demonstrated the best performance. A total of 14 features were retained after the least absolute shrinkage and selection operator (LASSO) selection. The final model exhibited robust predictive capability for identifying HF risk, with an overall accuracy of 0.731, a precision of 0.770, and an AUC (95% confidence interval (CI)) of 0.763 (0.676-0.840).
CONCLUSION: The GBM-based explainable prediction model demonstrated robust performance in predicting HF risk among patients with hypertension.