Development and validation of an interpretable machine learning model for early risk prediction of acute myocardial infarction.

BACKGROUND

Acute myocardial infarction (AMI) remains a leading cause of global morbidity and mortality, with early prediction critical for timely intervention. Traditional risk assessment tools are limited because of their reliance on limited variables and static thresholds. This study aims to develop an interpretable machine learning (ML) model using multidimensional clinical data for early AMI risk prediction.

METHODS

We performed a retrospective cohort study of 7939 patients enrolled from the second hospital of Shandong University from January 2020 to January 2024. A total of 108-dimensional clinical features composed of epidemiological data and biochemical data were collected, followed by data preprocessing. ML models were constructed via various algorithms with GridSearchCV hyperparameter tuning, and model performance was evaluated via 5-fold cross-validation. The SHapley Additive exPlanations (SHAP) method was employed to interpret the model, and the top 10 features were selected to simplify the model and maximize predictive performance. The final model was externally validated using an independent cohort of 532 patients collected from January 2025 to April 2025.

RESULTS

The weighted model with the XGBoost algorithm achieved the best performance, with accuracy of 0.864, F1-score value of 0.797, and prediction uncertainty lower than 0.01 on the test set. SHAP analysis revealed nonlinear interactions among metabolic profile, coagulation status, and demographic factor, identifying Hs-cTnI as the primary AMI predictor alongside NT-proBNP, LDL-C, CG, D-dimer, AST, PLT, GLU, female sex, and BMI. The optimal model showed wide applicability and strong robustness, confirmed by the accuracy of 0.932 on the independent validation dataset (n = 488 in applicability domain). An interactive webserver embedded with the optimal model was developed to enhance practicability (https://www.mips.net.cn).

CONCLUSIONS

An explainable ML model effectively predicted AMI risk integrating multimodal clinical data, offering a publicly accessible webserver generated for the optimal model facilitated its utility in clinical settings.