Hybrid Feature Selection and Classifying Stages through Electrocardiogram (ECG) Signal for Heart Disease Prediction

Diseases are major causes for increasing mortality rates. Clinical data analysis must predict cardiovascular disease. Machine learning (ML) may aid decision making and prediction using the healthcare field’s massive data set. ECG demonstrates electrical activities in human hearts, and variations in...

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Bibliographic Details
Published in:Engineering proceedings Vol. 59; no. 1; p. 126
Main Authors: Babu Kumar, Radhakrishnan Soundararajan, Kanimozhi Natesan, Roobini Maridhas Santhi
Format: Journal Article
Language:English
Published: MDPI AG 01.12.2023
Subjects:
cuckoo search algorithm
ECG signal
heart disease
modified chicken swarm optimization algorithm
multi-module neural network system
ISSN:2673-4591
Online Access:Get full text
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Summary:Diseases are major causes for increasing mortality rates. Clinical data analysis must predict cardiovascular disease. Machine learning (ML) may aid decision making and prediction using the healthcare field’s massive data set. ECG demonstrates electrical activities in human hearts, and variations in signals’ morphologies have provided improved knowledge of different types of arrhythmia depending on the state of the heart. In order to accurately forecast cardiac disorders, this study effort proposed a hybrid feature selection model and classification together with the ECG wave graph. QRS waves, which are time intervals of binary data, can be determined using the suggested technique of determining the ECG signal’s time interval from R-peak levels to the next level using double squared differences in signals. This approach involves many rounds of data sorting for decreasing noise, thresholding an ECG difference signal by examining the time interval between QRS, and then comparing relative magnitudes to identify the area of interval processing to evaluate accuracy results. In order to choose the best features, a modified chicken swarm optimization algorithm (MCSO) was proposed. Aberrant waves caused by cardiac ailments impacted the dataset patients, according to the suggested research’s unique machine learning methods of multi-module neural network system (MMNNS). The dataset was collected from the ML repository dataset vault at UCI as an individual ECG signal from the Heart Database. The findings demonstrate that each approach has a particular advantage in achieving the aims that have been set out.
ISSN:2673-4591
DOI:10.3390/engproc2023059126