Study On AI-Assisted Health Detection Mechanism Based On ECG Data For Public Health

Abstract

Health care is being revolutionized by Artificial Intelligence (AI) technologies that provide more precise diagnosis, customized therapies, streamlined administrative procedures and better patient care. A health detection mechanism assesses electrocardiogram (ECG) data to detect cardiac anomalies, offering precise and immediate atrial fibrillation diagnosis. We propose artificial intelligence (AI) assisted public health monitoring using ECG data. This study introduces the dynamic Ant Colony Optimized Intelligent Gaussian Naïve Bayes (DACO-IGNB) method to identify the atrial fibrillation detection mechanism. The Atrial Fibrillation (AF) ECG data were collected and Baseline Wander was to facilitate pre-processing. The Principal Component Analysis (PCA) method was used to extract the feature. Compared to other traditional methods, it demonstrated superior performance across accuracy (90.9%), F1-Score (0.87), recall (0.89) and precision (0.88) metrics. These findings underscore its efficiency in DACO-IGNB assisting health detection and diagnostics, significantly improving the accuracy and reliability of public health monitoring systems.