Cardiac arrhythmias impose a significant burden on the healthcare environment due to the increasing ratio of mortality worldwide. Arrhythmia and abnormal ECG heartbeat are the possible symptoms of severe heart diseases that can lead to death. Premature ventricular contraction (PVC) is a common form of cardiac arrhythmia which begins from the lower chamber of the heart, and frequent occurrence of PVC beat might lead to mortality. ECG signals are the noninvasive and primary tool used to identify the actual life threat related to the heart. Nowadays, in society, the computer-assisted technique reduces doctors' burden to evaluate heart disease and heart arrhythmia automatically. Regardless of well-equipped and well-developed health facilities that are available for monitoring the cardiac condition, the success stories are yet unsatisfactorily due to the complexity of the cardiac disorder. The most challenging part in ECG signal analysis is to extract the accurate features relevant to the arrhythmia for classification due to the inter-patient variation. There are many morphological changes present in the ECG signals. Hence, there is a gap in the usage of appropriate methods for the extraction of features and classification models, which reduce the biased diagnosis of PVC arrhythmia. To predict PVC arrhythmia accurately is a quite challenging task owing to (a) QRS negative (b) long compensatory pause (c) p-wave (d) biased diagnosis of PVC detection due to the small feature set. This study presents a new approach for PVC prediction using derived predictor variables from the electrocardiograph (ECG-MLII) signals: R–R wave interval, previous R–R wave interval, QRS duration, and verification of P-wave whether it is present or absent using threshold technique. We propose the machine learning-data mining MACDM integrated approach using five different models of multiple logistic regression and four classifiers, namely, Random Forest (RF), K-Nearest Neighbor (KNN), Support vector machine (SVM), and Naïve Bayes (NB). The experiment was conducted on the public benchmark MIT-BIH-AR to evaluate the performance of our proposed MACDM technique. The multiple logistic regression models constructed as a function of all independent variables achieved an accuracy of 99.96%, sensitivity 98.9%, specificity 99.20%, PPV 99.25%, and Youden's index parameter 98.24%. Thus, it is proved that this computer-aided method helps our medical practitioners improve the efficiency of their services.

由于全球死亡率的增加，心脏心律失常给医疗环境带来了沉重负担。心律不齐和心电图异常是可能导致死亡的严重心脏病的症状。室性早搏（PVC）是心律不齐的常见形式，始于心脏下腔，频繁发生PVC搏动可能导致死亡。ECG信号是用于识别与心脏相关的实际生命威胁的非侵入性主要工具。如今，在社会上，计算机辅助技术减轻了医生自动评估心脏病和心律不齐的负担。不管有什么设备完善和完善的保健设施可用于监测心脏状况，由于心脏疾病的复杂性，成功案例还不能令人满意。心电图信号分析中最具挑战性的部分是提取与心律不齐相关的准确特征，以进行归因于患者之间的差异。ECG信号中存在许多形态变化。因此，在使用适当的方法来提取特征和分类模型方面存在差距，这减少了PVC心律失常的偏倚诊断。由于（a）QRS阴性（b）长时间的补偿性停顿（c）p波（d）由于特征集小，偏向诊断PVC，准确预测PVC心律失常是一项非常具有挑战性的任务。这项研究提出了一种使用心电图仪（ECG-MLII）信号推导的预测变量进行PVC预测的新方法：R–R波间隔，先前的R–R波间隔，QRS持续时间，以及使用阈值技术验证P波是否存在。我们提出了使用五种不同的多元Logistic回归模型和四个分类器的机器学习数据挖掘MACDM集成方法，即随机森林（RF），K最近邻（KNN），支持向量机（SVM）和朴素贝叶斯（NaïveBayes）（注意）。实验是在公开基准MIT-BIH-AR上进行的，旨在评估我们提出的MACDM技术的性能。构造为所有自变量的函数的多元逻辑回归模型实现了99.96％的准确性，99.8％的敏感性，99.20％的特异性，PPV 99.25％的准确性以及Youden指数参数98.24％的准确性。因此，证明了这种计算机辅助方法可以帮助我们的医生提高服务效率。