Heart Rate Variability-Based Machine Learning Approach for Sudden Cardiac Arrest Prediction

This study illustrates a novel application of machine learning: the prediction of sudden cardiac arrest (SCA) using HRV features. SCA is a life-threatening condition that calls for rapid medical intervention. Patients with SCA who get an early diagnosis have a far better prognosis. HRV known as Heart Rate Variability analysis is used in this study to diagnose cardiac abnormalities and assess autonomic nervous system dysfunction. Prediction models built with machine learning algorithms might aid clinicians in identifying people at risk for cardiac arrest. The strategy involves gathering and preparing data, extracting and choosing features, and training and evaluating models. The experimental prediction performance of the proposed model may be seen using a variety of performance metrics including accuracy, sensitivity, and specificity. The clinical significance of the proposed machine learning approach to SCA prediction is addressed, along with its limitations and recommended future lines of inquiry.