Early Prediction of Cardiac Arrest Using Data Mining Algorithms

Abstract

Cardiac arrest is a sudden and unexpected loss of heart function that can lead to death. Early prediction of cardiac arrest could help to improve survival rates by allowing for early intervention. Datamining is a field of computer science that involves the extraction of knowledge from large datasets. Data mining algorithms can be used to identify patterns in data that may be indicative of cardiac arrest. For example, data mining algorithms can be used to identify patients who are at increased risk of cardiac arrest based on their medical history, lifestyle factors, and other characteristics. This paper reviews the use of data mining algorithms for early prediction of cardiac arrest. The paper discusses the different data mining algorithms that have been used for this purpose, as well as the results of studies that have evaluated the effectiveness of these algorithms. The paper also discusses the challenges of using data mining for early prediction of cardiac arrest, and the future directions of research in this area. The paper concludes that data mining algorithms have the potential to improve early prediction of cardiac arrest. However, more research is needed to develop more accurate and reliable data mining algorithms. Additionally, more research is needed to develop methods for integrating data mining algorithms into clinical practice.