Advanced ECG Signal Analysis for Cardiovascular Disease Diagnosis Using AVOA Optimized Ensembled Deep Transfer Learning Approaches

The integration of IoT and Deep Learning (DL) has significantly advanced real-time health monitoring and predictive maintenance in prognostic and health management (PHM). Electrocardiograms (ECGs) are widely used for cardiovascular disease (CVD) diagnosis, but fluctuating signal patterns make classification challenging. Computer-assisted automated diagnostic tools that enhance ECG signal categorization using sophisticated algorithms and machine learning are helping healthcare practitioners manage greater patient populations. With this motivation, the study proposes a DL framework leveraging the PTB-XL ECG dataset to improve CVD diagnosis. Deep Transfer Learning (DTL) techniques extract features, followed by feature fusion to eliminate redundancy and retain the most informative features. Utilizing the African Vulture Optimization Algorithm (AVOA) for feature selection is more effective than the standard methods, as it offers an ideal balance between exploration and exploitation that results in an optimal set of features, improving classification performance while reducing redundancy. Various machine learning classifiers, including Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), and Extreme Learning Machine (ELM), are used for further classification. Additionally, an ensemble model is developed to further improve accuracy. Experimental results demonstrate that the proposed model achieves the highest accuracy of 96.31%, highlighting its effectiveness in enhancing CVD diagnosis.