Prediction of High-Risk Cardiac Arrhythmia Based on Optimized Deep Active Learning

Abstract

In contemporary society, numerous challenges are effectively addressed by applying computer science and artificial intelligence. Each year, a substantial number of fatalities occur due to various illnesses; however, many of these can be anticipated and managed through the utilization of AI technologies. Among the significant health concerns, cardiac arrhythmia is particularly noteworthy within the domain of cardiovascular diseases, which is the primary focus of this study. Cardiac arrhythmias are classified into distinct categories, with some being benign while others present considerable risks. This article proposes a novel methodology that employs advanced AI techniques for predicting high-risk cases of cardiac arrhythmia in individuals. The study delineates a new framework that integrates deep learning, fuzzy logic, and active learning, aiming to provide an innovative approach for the early detection of cardiac arrhythmias through Optimized Deep Active Learning (ODAL). The proposed ODAL framework achieved superior performance, with an F1 score of 90% for non-sinus rhythm classification and a precision of 92%. Additionally, the ROC analysis of the framework demonstrated satisfactory performance, achieving an accuracy of 86%, sensitivity of 86%, and specificity of 82%. These results underscore the model’s effectiveness in accurately identifying high-risk arrhythmia cases compared to other classifiers.