Detection of Heart Diseases Using Deep Learning Techniques

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'sessential benefit is reducing deaths due to heart disease because the heart is the most criticalpart of the human body. From this Starting point, this paper used electrocardiography todiagnose and predict heart disease. A system that supports deep learning by using ConvolutionalNeural Network and the use of the most critical global data set approved by previous researcherswas proposed to diagnose or predict the four most critical pathological conditions, namely (STTabnormalities, myocardial infarction (MI), arrhythmias, and Conduction disturbances andabnormalities) The proposed system goes through three primary stages (processing,classification, and prediction), where CNN deep learning algorithms of the design the proposedsystem. The data set was used. PTB-XL for calculating healthy and infected samples forcomplete system training, testing, and prediction. The proposed system achieved good resultswith a sensitivity of 72.3%, a specificity of 73.90%, an accuracy of 91.33%, an accuracy of88.69%, and an f1 score of 92.51%.