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Designing Machine Learning-Based Variable-Order Bayesian Network in Predicting Sudden Cardiac Arrest and Death
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Author(s): Abolfazl Mehbodniya (Kuwait College of Science and Technology, Kuwait), Julian L. Webber (Osaka University, Japan), Ravi Kumar (Jaypee University of Engineering and Technology, India)and Manikandan Ramachandran (SASTRA University (Deemed), India)
Copyright: 2022
Pages: 25
Source title:
Leveraging AI Technologies for Preventing and Detecting Sudden Cardiac Arrest and Death
Source Author(s)/Editor(s): Pradeep Nijalingappa (Bapuji Institute of Engineering and Technology, Davangere, India), Sandeep Kumar Kautish (Lord Buddha Education Foundation, Nepal), Mangesh M. Ghonge (Sandip Institute of Technology and Research Centre, India)and Renjith V. Ravi (MEA Engineering College, India)
DOI: 10.4018/978-1-7998-8443-9.ch008
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Abstract
Recent surveys suggest that the majority of the world's population is unconcerned with their health. Aside from a hectic lifestyle, research reveals that stress is also a component in the development of many diseases. Sudden cardiac arrest and death (SCD) is a major public health concern that jeopardizes patient safety. As a result, detecting such illnesses only by ECG is difficult. The Bayesian Dirichlet equivalence score, AIC (akaike information criterion), and MDL (maximum description length) scores make up the variable-order Bayesian network (VOBN). On the basis of HRV (heart rate variability) acquired from ECG and using a hybrid classifier to identify SCD patients from normal patients, this study predicts sudden cardiac arrest before it occurs within 30 minutes. The validity of the suggested study is checked using the physionet database of cardiac patients and normal people, as well as the Cleveland dataset. The proposed method achieves 97.1% accuracy, 96.2% precision, 89.8% recall, 84.82% F1-score, 54.66% AUC, and 45.92% ROC, according to the results.
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