A proposed heart disease diagnosis based on Deep learning. | ||||
| International Journal of Telecommunications | ||||
| Volume 04, Issue 02, July 2024, Page 1-12 PDF (1.54 MB) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/ijt.2024.293170.1054 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Mohamed Ebrahim ELBouridy   1; Amira Salah EL-Batouty 2; Marwa E Samara1; Wael Abouelwafa Ahmed3; Mohamed Ahmed Massoud4
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| 1Alexandria Higher Institute of Engineering and Technology | ||||
| 2Alexandria Higher Institute of Engineering &Technology, Alex | ||||
| 3Department of Biomedical Engineering, Faculty of Engineering, Minia University, Minia, Egypt | ||||
| 4Department of Biomedical Engineering, Faculty of Engineering, Minia University, Minia, Egypt. | ||||
| Abstract | ||||
| One of the most influential factors in preserving human life is many diseases, including car-diovascular diseases, where delay in discovering these diseases is considered one of the most important factors that lead to death. The level of cholesterol in the blood, age, gender, as well as the level of sugar in the blood and heart rate are among the factors that most affect the incidence of heart disease. The accurate diagnosis of all these diseases depends on the experience and skill of the treating physician. Many researchers have intended to use automated methods to diagnose diseases without relying on the expertise of doctors. In this research, researchers present a proposal based on deep learning (DL) using the distinctive features of some factors affecting heart disease. Therefore, magnification techniques have been used to diagnose whether a patient is at risk for cardiovascular disease. Bloody or not. The research resulted in progress, as the accuracy rate in diagnosis reached 90.088%. | ||||
| Keywords | ||||
| Convolutional neural network (CNN)1; Sparse autoencoder2; deep learning (DL)3; and heart disease (HD)4 | ||||
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