用可解释的集成学习模型，自动识别心电图中的宽QRS波心动过速。
CardioForest: An Explainable Ensemble Learning Model for Automatic Wide QRS Complex Tachycardia Diagnosis from ECG
- 基于优化随机森林的CardioForest模型，融合多种机器学习方法。
- 测试准确率达95.19%，召回率78.42%，且能识别关键心电特征。
- 通过SHAP分析实现结果可解释，适合临床紧急诊断场景使用。
本研究旨在开发并评估一种基于集成学习的框架，用于从心电图（ECG）信号中自动检测宽QRS波心动过速（WCT），强调诊断准确性和可解释性。所提系统整合了优化后的随机森林（CardioForest）及XGBoost、LightGBM等模型，基于公开的MIMIC-IV数据集进行训练与测试。评估指标包括准确率、平衡准确率、精确率、召回率、F1分数、ROC-AUC及误差率（RMSE、MAE）。CardioForest在所有指标中表现最佳，测试准确率为95.19%，平衡准确率为88.76%，精确率为95.26%，召回率为78.42%，ROC-AUC为0.8886。通过SHAP分析验证了模型对关键心电特征（如QRS持续时间）的排序符合临床直觉，增强了临床可信度与实用性。研究证实CardioForest是高度可靠且可解释的WCT检测模型，能在高风险紧急场景中辅助心内科医生快速做出精准判断。
原文摘要 · Abstract (English)
This study aims to develop and evaluate an ensemble machine learning-based framework for the automatic detection of Wide QRS Complex Tachycardia (WCT) from ECG signals, emphasizing diagnostic accuracy and interpretability using Explainable AI. The proposed system integrates ensemble learning techniques, i.e., an optimized Random Forest known as CardioForest, and models like XGBoost and LightGBM. The models were trained and tested on ECG data from the publicly available MIMIC-IV dataset. The testing was carried out with the assistance of accuracy, balanced accuracy, precision, recall, F1 score, ROC-AUC, and error rate (RMSE, MAE) measures. In addition, SHAP (SHapley Additive exPlanations) was used to ascertain model explainability and clinical relevance. The CardioForest model performed best on all metrics, achieving a test accuracy of 95.19%, a balanced accuracy of 88.76%, a precision of 95.26%, a recall of 78.42%, and an ROC-AUC of 0.8886. SHAP analysis confirmed the model's ability to rank the most relevant ECG features, such as QRS duration, in accordance with clinical intuitions, thereby fostering trust and usability in clinical practice. The findings recognize CardioForest as an extremely dependable and interpretable WCT detection model. Being able to offer accurate predictions and transparency through explainability makes it a valuable tool to help cardiologists make timely and well-informed diagnoses, especially for high-stakes and emergency scenarios.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。