This paper presents a high-performance algorithm for diagnosing coronary artery disease (CAD) through a blindfold strategy and subject-specific data with ECG signals.In the proposed method,multi-grained scanning with sliding window is constructed to ensure the validity of the features extracted in the ECG segments.Completely random forests and random forests are used for extracting diverse features automatically.This allows the proposed algorithm to achieve excellent detection accuracy with subject-specific data and different-scale training data. Moreover, the gain comparison of the cascade forests is used inherently to optimize model parameters automatically, thereby avoiding errors caused by the manual setting of parameters. The proposed algorithm achieves an accuracy with 100% when 10% of the training data are used. Even in the case where the training set ratio is 0.1%, the detection accuracy of the proposed model is 99.86%. Additionally, the classification performance of the proposed algorithm on subject-specific data reaches 99.98%. Due to its robustness to perturbations in the scale of the training data and efficiency regarding specific subject data, the proposed system is applicable to CAD diagnosis.

本文提出了一种通过眼罩策略和带有心电信号的特定对象数据来诊断冠状动脉疾病 (CAD) 的高性能算法。在心电图段中提取。完全随机森林和随机森林用于自动提取不同的特征。这使得所提出的算法能够在针对特定主题的数据和不同规模的训练数据时实现出色的检测精度。而且，级联森林的增益比较固有地用于自动优化模型参数，从而避免因手动设置参数而导致的错误。当使用 10% 的训练数据时，所提出的算法达到了 100% 的准确率。即使在训练集比率为 0.1% 的情况下，所提出模型的检测精度也为 99.86%。此外，所提出的算法对特定主题数据的分类性能达到了 99.98%。由于其对训练数据规模扰动的鲁棒性和特定主题数据的效率，所提出的系统适用于 CAD 诊断。