Prediction of Heart Disease Using Deep Convolutional Neural Networks

Heart diseases are currently a major cause of death in the world. This problem is severe in developing countries in Africa and Asia. A heart disease predicted at earlier stages not only helps the patients prevent it, but I can also help the medical practitioners learn the major causes of a heart att...

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Bibliographic Details
Published in:Arabian journal for science and engineering (2011) Vol. 46; no. 4; pp. 3409 - 3422
Main Authors: Mehmood, Awais, Iqbal, Munwar, Mehmood, Zahid, Irtaza, Aun, Nawaz, Marriam, Nazir, Tahira, Masood, Momina
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2021
Springer Nature B.V
Subjects:
Algorithms
Artificial neural networks
Cardiovascular disease
Developing countries
Engineering
Heart
Heart diseases
Humanities and Social Sciences
LDCs
Machine learning
multidisciplinary
Neural networks
Performance evaluation
Research Article-Computer Engineering and Computer Science
Science
Health care system
Convolutional neural networks
Heart disease
Machine learning
ISSN:2193-567X, 1319-8025, 2191-4281
Online Access:Get full text
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Summary:Heart diseases are currently a major cause of death in the world. This problem is severe in developing countries in Africa and Asia. A heart disease predicted at earlier stages not only helps the patients prevent it, but I can also help the medical practitioners learn the major causes of a heart attack and avoid it before its actual occurrence in patient. In this paper, we propose a method named CardioHelp which predicts the probability of the presence of cardiovascular disease in a patient by incorporating a deep learning algorithm called convolutional neural networks (CNN). The proposed method is concerned with temporal data modeling by utilizing CNN for HF prediction at its earliest stage. We prepared the heart disease dataset and compared the results with state-of-the-art methods and achieved good results. Experimental results show that the proposed method outperforms the existing methods in terms of performance evaluation metrics. The achieved accuracy of the proposed method is 97%.
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ISSN:2193-567X
1319-8025
2191-4281
DOI:10.1007/s13369-020-05105-1