Prediction and Analysis of Heart Attack using Various Machine Learning Algorithms

Abstract

The healthcare industry is typically thought of as "information rich" yet "knowledge poor." The healthcare systems include a vast amount of data. However, the lack of efficient analysis tools makes the employability challenging to uncover hidden linkages and patterns in the data. In the business and scientific realms, data mining and information retrieval have various applications. The use of data mining methods in the health service can produce insightful information. The possible applications for rule based, tree structure, Bayesian networks, and artificial neural networks classification are briefly discussed in this research based data mining approaches to a large volume of healthcare data. Huge amounts of healthcare data are gathered by the industry, but they are regrettably not "mined" to find hidden information. Heart attack is a primary causes of unexpected mortality, especially in women, heart attack prediction is crucial in nations with low incomes. Despite using common clinical techniques like electrocardiography and the research goal is to identify the finest machine learning algorithm for predicting heart attacks.