Leveraging AI and Machine Learning to Predict and Prevent Sudden Cardiac Arrest in High-Risk Populations

Abstract

Cardiac arrest claims a lot of lives, mainly among people with heart diseases, diabetes, or abnormal heartbeats. Using AI and ML helps us find people at risk of heart failure and helps them avoid experiencing this condition. The research review looks at what AI and ML methods currently do to forecast SCA and checks how well they work. The models work by using lots of collected health information - from medical records, user monitors, and genetic studies - to forecast SCA danger right when needed. AI technology with machine learning algorithms can study large patient data to spot unique patterns that show which people are more likely to develop SCA. These models show two abilities: they estimate chance of events happening and suggest what steps to take to reduce risks. The study looks at AI implementation issues, including keeping patient data confidential, connecting with how doctors work today, and making sure doctors understand how AI systems make decisions. The research stresses that professionals should make transparent how their AI decisions work. Robust artificial intelligence and machine learning tools can help healthcare improve patient care by spotting those at highest risk of dangerous heart failure sooner and lowering deaths linked to cardiac emergencies.