@Article{cmes.2023.026535,
AUTHOR = {Muhammad Tayyeb, Muhammad Umer, Khaled Alnowaiser, Saima Sadiq, Ala’ Abdulmajid Eshmawi, Rizwan Majeed, Abdullah Mohamed, Houbing Song, Imran Ashraf},
TITLE = {Deep Learning Approach for Automatic Cardiovascular Disease Prediction Employing ECG Signals},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {137},
YEAR = {2023},
NUMBER = {2},
PAGES = {1677--1694},
URL = {http://www.techscience.com/CMES/v137n2/53353},
ISSN = {1526-1506},
ABSTRACT = {Cardiovascular problems have become the predominant cause of death worldwide and a rise in the number of
patients has been observed lately. Currently, electrocardiogram (ECG) data is analyzed by medical experts to
determine the cardiac abnormality, which is time-consuming. In addition, the diagnosis requires experienced
medical experts and is error-prone. However, automated identification of cardiovascular disease using ECGs is
a challenging problem and state-of-the-art performance has been attained by complex deep learning architectures.
This study proposes a simple multilayer perceptron (MLP) model for heart disease prediction to reduce computational complexity. ECG dataset containing averaged signals with window size 10 is used as an input. Several
competing deep learning and machine learning models are used for comparison. K-fold cross-validation is used to
validate the results. Experimental outcomes reveal that the MLP-based architecture can produce better outcomes
than existing approaches with a 94.40% accuracy score. The findings of this study show that the proposed system
achieves high performance indicating that it has the potential for deployment in a real-world, practical medical
environment.},
DOI = {10.32604/cmes.2023.026535}
}