Title: Detection and classification of arrhythmia disorders using machine learning algorithm

Authors: P. Ramani; S. Sugumaran; Manoharan Nivethitha Devi; T.J. Nagalakshmi; G. Annapoorani

Addresses: Electronics and Communication Engineering Department, SRM Institute of Science and Technology, Ramapuram, Chennai, India ' Electronics and Communication Engineering Department, Vishnu Institute of Technology, Bhimavaram, India ' Electronics and Instrumentation Engineering Department, St. Joseph's College of Engineering, Chennai, India ' Electronics and Communication Engineering Department, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, India ' Electronics and Communication Engineering Department, SRM Institute of Science and Technology, Ramapuram, Chennai, India

Abstract: A recent study by the United Nations Agency (World Health Organization) reported that 17.9 million people died due to cardiovascular disease, and it is increasing exponentially. Furthermore, it was also reported that it was highly difficult to recognise the sickness and dictate the relevant care in a timely manner. For analysis, a user data file for cardiopathy prediction that contains parameters such as gender, age, kind of pain, force per unit area, hyperglycaemia, and so on has been considered. The approach entails determining the correlations between the numerous properties of the data file using regular processing techniques and then treating the attributes appropriately to forecast the likelihood of cardiopathy. This article endeavours at probing methodised data-mining techniques such as NB classifier, random forest classification, decision tree in addition to support vector machine. These machine learning approaches require less time to anticipate sickness with a high degree of accuracy. The proposed algorithm provides 91.2% recognition rate than SVM and decision tree classifier.

Keywords: support vector machine; SVM; NB classifier; random forest; RF; arrhythmia disorders; decision tree.

DOI: 10.1504/IJMEI.2024.140806

International Journal of Medical Engineering and Informatics, 2024 Vol.16 No.5, pp.424 - 439

Received: 26 Jan 2022
Accepted: 23 Apr 2022
Published online: 03 Sep 2024 *

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