Key result
Reinforcement learning-tuned ECG model classifies acute coronary syndrome with an AUROC of ~0.86.
Why the study?
Deep learning applied to ECG for ACS diagnosis shows strong performance but limited clinical adoption due to lack of interpretability and robustness, while classical machine learning offers explainability but lower accuracy.
Does a multi-stage reinforcement learning-guided optimization framework improve the performance of random forest models for ACS classification from 12-lead ECGs compared to conventional baseline models?
Does a multi-stage reinforcement learning-guided optimization framework improve the performance of random forest models for ACS classification from 12-lead ECGs compared to conventional baseline models?
A reinforcement learning-guided optimization framework enhances the diagnostic accuracy and stability of explainable random forest models for detecting acute coronary syndrome on 12-lead ECGs.
BACKGROUND: Deep learning applied to ECG has demonstrated strong diagnostic performance for acute coronary syndrome (ACS), but widespread clinical adoption remains limited by lack of interpretability and model robustness. Classical machine learning approaches, such as random forests (RF), offer greater explainability but often underperform compared with deep learning models. OBJECTIVE: To develop and evaluate a structured, multi-stage hyperparameter optimization framework for a reinforcement learning-enhanced RF (RL-RF) model to improve the accuracy, stability, and interpretability of ACS classification from 12‑lead ECGs. METHODS: We analyzed 10,393 ECGs from 7397 patients (15.6% ACS prevalence), partitioned into training (80%), validation (10%), and test (10%) sets using stratified sampling. A five-stage optimization pipeline was implemented, including (1) initial hyperparameter exploration, (2) reinforcement learning-based optimization using a Light Double Deep Q-Network, (3) multi-local refinement on the full training set, (4) stability evaluation via multi-seed averaging, and (5) final ensemble construction. Model performance was compared against conventional RF and untuned RL-RF baseline using area under ROC curve, average precision (AP), and F1-score. Performance metrics were computed with a threshold that maximized the F1-score for each model. RESULTS: The tuned RL-RF model achieved superior performance on the independent test set, with an AUROC of 0.86, AP of 0.69, and F1-score of 0.58. The model demonstrated improved specificity and positive predictive value compared with baseline models, indicating enhanced ability to reduce false-positive classifications, with a modest decrease in sensitivity. CONCLUSIONS: A multi-stage reinforcement learning-guided optimization framework significantly improves the performance and stability of explainable RF models for ACS classification. This approach bridges the gap between accuracy and interpretability, supporting the development of clinically trustworthy AI-ECG systems.
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Daoud et al. (2026) studied Acute coronary syndrome (ACS) (n=7,397). Reinforcement learning-enhanced random forest (RL-RF) model vs. Conventional random forest and untuned RL-RF baseline models was evaluated on Area under the ROC curve (AUROC), average precision (AP), and F1-score for ACS classification. A tuned reinforcement learning-enhanced random forest model for acute coronary syndrome classification from 12-lead ECGs achieved an AUROC of 0.86, average precision of 0.69, and F1-score of 0.58.
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