Early Prediction of Cardiac Arrest Using Data Mining Algorithms

Abstract

Cardiac arrest is a potentially fatal loss of heart function that occurs suddenly and without warning. Predicting cardiac arrest early could increase the likelihood of survival and allow for prompt treatment. The discipline of computer science known as “datamining” focuses on the process of gleaning useful information from massive databases. Algorithms for data mining can be used to look for trends in records that can indicate a cardiac arrest. Patients at high risk of cardiac arrest due to their medical history, lifestyle choices, or other variables can be pinpointed, for instance, with the help of data mining algorithms. Prediction of cardiac arrest using data mining techniques is discussed in this research. The chapter talks about the many data mining methods that have been employed for this, and the studies that have evaluated their efficacy. The chapter also covers the difficulties of employing data mining for early prediction of cardiac arrest, as well as potential future research avenues.