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Machine learning-data mining integrated approach for premature ventricular contraction prediction

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Abstract

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.

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Acknowledgements

The authors would like to thank anonymous reviewers for their valuable comments and Dr. Fawad Ali Khan from the University of Malaya, faculty of computer science and Information Technology, for their support and courage.

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Authors and Affiliations

  1. Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia

    Qurat-ul-ain Mastoi

  2. Department of Information Technology, Dadu Campus, University of Sindh, Jamshoro, Pakistan

    Muhammad Suleman Memon

  3. School of Computer Science and Engineering, Southeast University, Nanjing, China

    Abdullah Lakhan

  4. College of Computer Science and Information Technology, University of Anbar, 11, Ramadi, Anbar, Iraq

    Mazin Abed Mohammed

  5. Department of Software Engineering, Faculty of Engineering, University of Sindh, Jamshoro, Pakistan

    Mumtaz Qabulio

  6. Research Center for AI and IoT, Near East University, Mersin 10, Nicosia, Turkey

    Fadi Al-Turjman

  7. College of Agriculture, Al-Muthanna University, Samawah, 66001, Iraq

    Karrar Hameed Abdulkareem

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  1. Qurat-ul-ain Mastoi
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Mastoi, Qua., Memon, M.S., Lakhan, A. et al. Machine learning-data mining integrated approach for premature ventricular contraction prediction. Neural Comput & Applic 33, 11703–11719 (2021). https://doi.org/10.1007/s00521-021-05820-2

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