There have been many connections between medical science and artificial intelligence in recent years. Many problems arise with the integrity of communication. Cardiac arrhythmia, carried out using artificial intelligence methods, is one of the most dangerous diseases in the field of prevention. Topics introduced in artificial intelligence are the automatic selection of balancing and classification algorithms. In this study, metrics for machine learning algorithm selection are presented. The first problem is the problem of choosing the best balancing algorithm to balance the data sets, introduced as triangle rate (TR). The second issue to be studied is selecting the best automatic classification algorithm. The third action was to use a scoring algorithm to predict sinus and non-sinus arrhythmias. The heptagonal reinforcement learning (HRL) achieved results competitive with standard algorithms by combining three types of algorithms. The data used in this study was a 12-lead electrocardiogram (ECG) database of arrhythmias. The number of patients examined in this dataset is 10,646. The HRL algorithm has improved the previous algorithms by 5%, achieving 86% cardiac arrhythmia prediction.

近年来，医学与人工智能之间存在着许多联系。许多问题都是由于通信的完整性而产生的。使用人工智能方法进行的心律失常是预防领域中最危险的疾病之一。人工智能中引入的主题是平衡和分类算法的自动选择。在这项研究中，提出了机器学习算法选择的指标。第一个问题是选择最佳平衡算法来平衡数据集的问题，称为三角率（TR）。要研究的第二个问题是选择最佳的自动分类算法。第三个行动是使用评分算法来预测窦性和非窦性心律失常。七边形强化学习（HRL）通过结合三种类型的算法，取得了与标准算法相媲美的结果。本研究中使用的数据是心律失常的 12 导联心电图 (ECG) 数据库。该数据集中检查的患者人数为 10,646 人。 HRL算法较之前的算法提升了5%，实现了86%的心律失常预测。