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March 3, 2026Open Access

Development and Validation of a Machine Learning Model to Predict the Risk of Medical Decision-Making Delay in Acute Myocardial Infarction Patients From Multicenter Tertiary Hospitals in China

Key Points

Medical decision-making delay was found in 61.78% of acute myocardial infarction patients, indicating substantial treatment risks.
The random forest model achieved an impressive AUC of 0.91, demonstrating superior predictive accuracy compared to other methods.

Structured PICO

Can machine learning models accurately predict the risk of medical decision-making delay in patients with acute myocardial infarction?

Population

594 acute myocardial infarction (AMI) patients admitted to multicenter tertiary hospitals in Hainan, China, from January to August 2023.

Intervention

Machine learning predictive models (logistic regression, random forest, support vector machine, XGBoost, decision tree, and naive Bayes) to quantify the risk of pre-hospital decision-making delay.

Outcome

Area Under the Curve (AUC) for predicting medical decision-making delay.

A random forest machine learning model can accurately predict the risk of pre-hospital medical decision-making delay in AMI patients, identifying pain severity, disease type, and prior MI history as the most significant predictors.

Abstract

Purpose: Timely reperfusion is critical for improving outcomes in patients with acute myocardial infarction (AMI), as every delay raises the incidence of complications and mortality. Therefore, we aimed to develop a machine-learning model that quantifies the risk of pre-hospital decision-making delay and visualizes how individual determinants modulate this risk. Patients and Methods: This retrospective study included 594 AMI patients admitted to hospitals in Hainan from January to August 2023. Data were collected via medical systems and surveys. We used the Elastic Net and Boruta algorithms for feature selection and hyperparameter optimization with grid search and 10-fold cross-validation. Six machine learning models were developed: logistic regression, random forest, support vector machine, XGBoost, decision tree, and naive Bayes. The primary metric was the Area Under the Curve (AUC), and SHapley Additive exPlanations (SHAP) were used to assess feature importance. Results: The medical decision-making delay rate was 61.78%, with a median decision time of 3.98 hours. All models showed good predictive performance, with the random forest model excelling, achieving an AUC of 0.91, accuracy of 0.92, recall of 0.98, F1 score of 0.93, and specificity of 0.81. SHAP analysis revealed that pain severity, disease type, and history of myocardial infarction were the most significant predictors of delay. Pain severity had a nonlinear relationship with delay risk, while disease type and prior infarction history showed complex interactions. Conclusion: Machine learning models, especially random forest, accurately predict the risk of delayed medical decision-making in AMI patients and reliably delineate the key drivers of such delay, thereby informing targeted clinical interventions. Keywords: machine learning, predictive model, acute myocardial infarction, AMI, medical decision-making delay

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