Performance Evaluation of Machine Learning Classifiers for Sudden Cardiac Arrest Detection

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.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Performance Evaluation of Machine Learning Classifiers for Sudden Cardiac Arrest Detection

Cited by 2 publications

References 20 publications

Unveiling the Enigma: Sudden Cardiac Arrest Among Youth and Uncovering Underlying Causes: A Data Analysis Study

Unveiling the Enigma: Sudden Cardiac Arrest Among Youth and Uncovering Underlying Causes: A Data Analysis Study

Visual Analysis of Cardiac Arrest Prediction Using Machine Learning Algorithms

Contact Info

Product

Resources

About