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Fusion-Based Machine Learning Architecture for Heart Disease Prediction

Authors
Nadeem, Muhammad WaqasGoh, Hock GuanKhan, Muhammad AdnanHussain, MuzammilMushtaq, Muhammad FaheemPonnusamy, Vasaki A. P.
Issue Date
May-2021
Publisher
TECH SCIENCE PRESS
Keywords
Heart disease; machine learning; support vector machine; fuzzy logic; fusion; cardiovascular
Citation
CMC-COMPUTERS MATERIALS & CONTINUA, v.67, no.2, pp.2481 - 2496
Journal Title
CMC-COMPUTERS MATERIALS & CONTINUA
Volume
67
Number
2
Start Page
2481
End Page
2496
URI
https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/81294
DOI
10.32604/cmc.2021.014649
ISSN
1546-2218
Abstract
The contemporary evolution in healthcare technologies plays a considerable and significant role to improve medical services and save human lives. Heart disease or cardiovascular disease is the most fatal and complex disease which it is hardly to be detected through our naked eyes, as numerous people have been suffering from this disease globally. Heart attacks occur when the ranges of vital signs such as blood pressure, pulse rate, and body temperature exceed their normal values. The efficient diagnosis of heart diseases could play a substantial role in the field of cardiology, while diagnostic time could be reduced. It has been a key challenge for researchers and medical experts to diagnose heart diseases accurately and timely. Therefore, machine learning-based techniques are used for the diagnosis with higher accuracy, using datasets compiled from former medical patients' reports. In recent years, numerous studies have been presented in the literature propose machine learning techniques for diagnosing heart diseases. However, the existing techniques have some limitations in terms of their accuracy. In this paper, a novel Support Vector Machine (SVM) based architecture for heart disease prediction, empowered with a fuzzy based decision level fusion, is presented. The SVMbased architecture has improved the accuracy significantly as compared to existing solutions, where 96.23% accuracy has been achieved.
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