Visual Analysis of Cardiac Arrest Prediction Using Machine Learning Algorithms

Abstract

A visual analysis may accurately predict cardiac arrest, making it a potent educational tool for raising public awareness of health issues. By predicting cardiac arrest earlier, preventative steps can be taken to save lives, and the dissemination of such health knowledge can dramatically lower the world mortality rate. A heart attack, also known as cardiac arrest, encompasses various heart-related disorders and has been the leading cause of death worldwide in recent decades. Several medical data mining and machine learning technologies are being applied to gather helpful knowledge regarding heart disease prediction. The accuracy of the intended outcomes, however, is insufficient. This chapter aims to predict the likelihood of patients having a heart disease to solve the issue. Specifically, it compared alternative models for the identification of cardiac arrest to appropriately categorize and forecast heart attack instances with compact features. The use of ensemble algorithms over classifier algorithms gives a maximum accuracy of 96.5%, which is examined in this investigation.