Open Access

ARTICLE

Fusion-Based Machine Learning Architecture for Heart Disease Prediction

Muhammad Waqas Nadeem^1,2, Hock Guan Goh^1,*, Muhammad Adnan Khan³, Muzammil Hussain⁴, Muhammad Faheem Mushtaq⁵, Vasaki a/p Ponnusamy¹

1 Faculty of Information and Communication Technology (FICT), Universiti Tunku Abdul Rahman (UTAR), Kampar, Perak, 31900, Malaysia
2 Department of Computer Science, Lahore Garrison University, Lahore, 54000, Pakistan
3 Department of Computer Science, Faculty of Computing, Riphah International University, Lahore Campus, Lahore, 54000, Pakistan
4 Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore, 54000, Pakistan
5 Department of Information Technology, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, 64200, Pakistan

* Corresponding Author: Hock Guan Goh. Email:

Computers, Materials & Continua 2021, 67(2), 2481-2496. https://doi.org/10.32604/cmc.2021.014649

Received 05 October 2020; Accepted 21 November 2020; Issue published 05 February 2021

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 SVM-based architecture has improved the accuracy significantly as compared to existing solutions, where 96.23% accuracy has been achieved.

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Citations