Fusion-Based Machine Learning Architecture for Heart Disease Prediction

TLDR

Heart disease is a leading cause of death worldwide, often undetectable by visual inspection, and its diagnosis relies on vital signs such as blood pressure, pulse, and temperature, making timely and accurate detection a major challenge in cardiology. This study aims to develop a machine‑learning approach for heart disease prediction. The authors propose a novel SVM‑based architecture enhanced with fuzzy decision‑level fusion, trained on historical patient datasets. The model achieved 96.23 % accuracy, surpassing existing methods.

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.