Title: Diagnosis and management of arrhythmia using machine learning

Authors: S. Pratyaksha; M.L. Chayadevi

Addresses: Department of Computer Science and Engineering, BNM Institute of Technology, Bangalore, Karnataka, India ' Department of Computer Science and Engineering, BNM Institute of Technology, Bangalore, Karnataka, India

Abstract: Cardiac arrhythmia refers to a variety of heart rhythm disorders in which the heartbeat is irregular, rapid or sluggish. Arrhythmias come in a variety of forms, some of which have no symptoms. When symptoms are present, palpitations or a sense of a pause between heartbeats may be noticeable. In more extreme instances, light-headedness, fainting, shortness of breath, or chest discomfort may develop. While most arrhythmias are harmless, some can cause serious complications such as stroke or heart failure. Others might lead to cardiac arrest. Arrhythmia affects millions of individuals throughout the world. Nearly half of all deaths due by cardiovascular disease, or roughly 15% of all deaths globally, are caused by sudden cardiac death. Ventricular arrhythmias account for approximately 80% of sudden cardiac death. Arrhythmias can affect people of any age, although they are more frequent as they get older.

Keywords: medical imaging; machine learning; arrhythmia diagnosis; KNN; SVM; random forest; decision tree.

DOI: 10.1504/IJCAT.2023.133039

International Journal of Computer Applications in Technology, 2023 Vol.72 No.2, pp.125 - 130

Received: 21 Aug 2022
Received in revised form: 04 Nov 2022
Accepted: 14 Nov 2022
Published online: 27 Aug 2023 *

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