Detection of Heart Diseases Using Deep Learning Techniques
DOI:
https://doi.org/10.24237/ASJ.02.04.795BKeywords:
lectrocardiography,, Diseases,Abstract
Electrocardiography is an effective tool for detecting heart diseases or predicting heart diseases,
and previous researchers have approved it as an effective tool in diagnosis. This early diagnosis's
essential benefit is reducing deaths due to heart disease because the heart is the most critical
part of the human body. From this Starting point, this paper used electrocardiography to
diagnose and predict heart disease. A system that supports deep learning by using Convolutional
Neural Network and the use of the most critical global data set approved by previous researchers
was proposed to diagnose or predict the four most critical pathological conditions, namely (STT
abnormalities, myocardial infarction (MI), arrhythmias, and Conduction disturbances and
abnormalities) The proposed system goes through three primary stages (processing,
classification, and prediction), where CNN deep learning algorithms of the design the proposed
system. The data set was used. PTB-XL for calculating healthy and infected samples for
complete system training, testing, and prediction. The proposed system achieved good results
with a sensitivity of 72.3%, a specificity of 73.90%, an accuracy of 91.33%, an accuracy of
88.69%, and an f1 score of 92.51%.
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