Efficient ECG Classification Based on Machine Learning and Feature Selection Algorithm for IoT-5G Enabled Health Monitoring Systems

With the rise of the Internet of Things (IoT) and Fifth Generation (5G) networks, real-time health monitoring has become more efficient, enabling continuous tracking of cardiac health.Electrocardiograms (ECG) are essential for diagnosing cardiovascular diseases, but classifying ECG signals presents challenges due to their high-dimensional features.Standard machine learning classifiers often struggle to achieve high accuracy, and IoT devices face resource constraints that hinder real-time processing.This paper introduces a novel methodology that combines Particle Swarm Optimization (PSO)-based feature selection with machine learning classifiers, such as K-Nearest Neighbors (KNN), Random Forest (RF), Decision Trees (DT), and Support Vector Machines (SVM).The proposed method uses the accuracy of the machine learning classifiers as the fitness function in the PSO algorithm.This ensures that the selected features are optimal and well-suited for the classifier, improving both classification accuracy and computational efficiency.The approach was validated using the MIT-BIH Arrhythmia dataset, achieving 98% accuracy with PSO-SVM and 84% without PSO-based feature selection.The dimensionality of the ECG dataset is reduced from 4000 features to 888, improving classification accuracy and computational efficiency.These results outperform current machine learning and deep learning methods, demonstrating the effectiveness of the proposed approach for arrhythmia detection.This research provides a scalable solution for IoT-enabled, 5G-powered health monitoring systems, enhancing both classification performance and real-time processing in resource-constrained environments.