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 94.95%, a balanced accuracy of 88.31%, and high precision and recall metrics. 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.

翻译：本研究旨在开发并评估一种基于集成机器学习的框架，用于从心电图信号中自动检测宽QRS波心动过速，重点利用可解释人工智能提升诊断准确性与可解释性。所提出的系统集成了集成学习技术，即一种名为CardioForest的优化随机森林，以及XGBoost和LightGBM等模型。模型使用公开可用的MIMIC-IV数据集中的心电图数据进行训练和测试。测试借助准确率、平衡准确率、精确率、召回率、F1分数、ROC-AUC及误差率（均方根误差、平均绝对误差）等指标进行。此外，研究采用SHAP（SHapley可加性解释）方法以确定模型的可解释性及临床相关性。CardioForest模型在所有指标上表现最佳，测试准确率达到94.95%，平衡准确率为88.31%，并具有较高的精确率和召回率。SHAP分析证实了该模型能够依据临床直觉对最相关的心电图特征（如QRS波时限）进行排序，从而增强了临床实践中的可信度与可用性。研究结果表明CardioForest是一种极其可靠且可解释的宽QRS波心动过速检测模型。其通过可解释性提供准确预测和透明决策的能力，使其成为协助心脏病专家（尤其是在高风险和紧急情况下）做出及时、明智诊断的有价值工具。