Convolutional Neural Network using Social Group Optimization for Electrocardiogram

Electrocardiogram (ECG) analysis plays a pivotal role in diagnosing various cardiac conditions, making it essential to develop accurate and robust classification models. This project proposes a novel approach to optimize ECG analysis by considering social group factors, leveraging the combined power of XG Boost classifier and Convolutional Neural Networks (CNNs). The methodology involves collecting a diverse dataset of ECG recordings, spanning various demographic groups. Preprocessing techniques are applied to standardize and clean the data, followed by feature extraction using CNNs to capture intricate patterns in the ECG signals. Subsequently, XG Boost classifier is employed to classify the ECG signals based on the extracted features, considering social group factors as additional input features.