Machine Learning Models for Early Detection of Cardiac Arrest Risk Factors

An interruption in the heart's electrical rhythm can result in cardiac arrest, which is potentially fatal. Effective management depends on accurately identifying high-risk patients, providing quick clinical care, and performing successful resuscitation. (ML) Researchers have used systems that are excellent at analyzing complex data to forecast the risk of cardiac arrest. Using real-time datasets from Kaggle, this study assessed four Machine Learning (ML) algorithms: Random Forest (RF), K-Nearest Neighbours (KNN), and Extreme Gradient Boost (XGBoost). The testing results showed that the RF-based ML technique displayed greater accuracy than existing algorithms. This demonstrates how can forecast the danger of cardiac arrest. ML algorithms can find patterns and risk factors that a human clinician might not be able to see. Healthcare professionals can build individualized preventive measures and interventions by utilizing these algorithms to acquire insightful information about a person's propensity for cardiac arrest.