Multi-faceted Review and Meta‑analysis to Predict Cardiac Outcomes using Machine Learning

Abstract

Acute cardiac arrest (CA) represents a significant challenge in medical prediction and intervention. Characterized by inadequate oxygen intake, CA often results in electrical imbalances within the heart, leading to irregular heartbeats and eventual cessation of blood flow. This condition can arise abruptly, necessitating prompt evaluation and intervention. Notably, cardiac arrest can occur without visible obstructions in the heart or brain, stemming instead from dysfunction in cardiac electrical activity and pumping efficiency. Individuals can be assessed for risk based on lifestyle, habits, and activities, although many may remain unaware of their susceptibility. Growing attention within the medical community reflects an increasing awareness of the need for early detection and preventive strategies. Clinicians utilize a variety of techniques to identify patients at risk of cardiac arrest, including electrocardiograms, Holter monitoring, echocardiography, and wearable technology. This paper reviews these methodologies and emphasizes the importance of early identification and risk assessment in improving outcomes for individuals at risk of acute cardiac arrest.