Predictive Analytics for Sudden Cardiac Arrest: AI and Machine Learning Approaches

Abstract

SCA kills many people each year because of heart diseases and diabetes-related 
heart problems. Using artificial intelligence and machine learning helps seek out 
patients at high risk of SCA so treatment can happen before cardiac arrest. This 
research analyzes the state-of-the-art AI and ML techniques for cardiac event 
forecasting and their efficiency. Specialized models use medical data such as 
patient records plus device and genetic data to provide precise risk findings at 
critical moments. With extensive data analysis Artificial Intelligence systems find 
warning signs of SCA and give forecast results as well as guidance to handle 
possible risks. The report identifies practical obstacles in using AI including 
maintaining personal patient data security while integrating technology with 
clinical work and training healthcare staff to interpret AI findings. AI transparency 
is essential to build trust and save more lives through healthcare because it finds 
cardiac arrest risks ahead of time to help patients receive early medical attention.