CardioForest: 一种用于从ECG信号自动检测宽QRS复波心动过速的可解释集成学习模型
CardioForest: An Explainable Ensemble Learning Model for Automatic Wide QRS Complex Tachycardia Diagnosis from ECG
AI总结：
CardioForest通过集成学习和SHAP分析，实现了高准确率的宽QRS复波心动过速检测，具有良好的可解释性和临床应用价值。
摘要：
本研究旨在开发和评估一种基于集成机器学习的框架，用于从ECG信号自动检测宽QRS复波心动过速（WCT），强调通过可解释人工智能实现诊断准确性和可解释性。所提出的系统集成了集成学习技术，即优化的随机森林，称为CardioForest，以及XGBoost和LightGBM等模型。这些模型在公开可用的MIMIC-IV数据集上的ECG数据上进行训练和测试。测试过程中使用了准确率、平衡准确率、精确率、召回率、F1分数、ROC-AUC以及误差率（RMSE、MAE）等指标进行评估。此外，SHAP（SHapley Additive exPlanations）被用来确定模型的可解释性和临床相关性。CardioForest模型在所有指标上表现最佳，达到了95.19%的测试准确率、88.76%的平衡准确率、95.26%的精确率、78.42%的召回率和0.8886的ROC-AUC。SHAP分析证实了该模型能够根据临床直觉对最相关的ECG特征（如QRS持续时间）进行排序，从而在临床实践中增强信任和可使用性。研究结果认可CardioForest作为一种极其可靠且可解释的WCT检测模型。能够通过可解释性提供准确的预测和透明性，使其成为帮助心内科医生做出及时且明智诊断的有价值的工具，特别是在高风险和紧急情况下。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
