Optimized Predictive Models in Health Care Using Machine Learning

14Fusion of Various Machine Learning Algorithms for Early Heart Attack Prediction

Monali Gulhane* and Sandeep Kumar

Department of CSE, Koneru Lakshmaiah Education Foundation, Vijayawada, India

Abstract

In this digital world, heart disease is one of the most pressing problems in human life. However, it is one of the leading causes of death for a significant number of individuals in a variety of countries across the world. This is because heart disease is one of the primary risk factors for stroke, another of the leading causes of mortality. The most recent developments in machine learning (ML) show that it is possible to make an accurate early diagnosis of cardiac disease by utilizing electrocardiogram (ECG) data in conjunction with patient information. Modern technological advances are primarily responsible for making it possible for these newly developed technologies to exist. Despite this, both the ECG and the data supplied by the patients are often inconsistent, which, in the end, renders it difficult for typical machine learning techniques to perform in a manner that is objective. Over time, many academics and industry experts working on the topic have uncovered a range of solutions at both the data and the algorithm levels. These solutions have been successful in addressing the issue. For the heart attack dataset that uses the UCI machine learning repository, we found that our accuracy increased by 93% when we assembled all three classifiers. We proposed to boost it. This ...