Advancing Early Cardiovascular Disease Prediction Model using Improved Beluga Whale Optimization with Ensemble Learning via ECG Signal Analytics

Cardiovascular Disease (CVD) mainly affects the blood vessels and heart such as coronary artery disease, stroke, and heart failure. Early recognition is vital for on-time intervention and enhanced patient results. CVD is a major issue in society nowadays. When compared to the non-invasive model, the electrocardiogram (ECG) is the most effective approach for identifying cardiac defects. However, ECG analysis needs an experienced person with high knowledge and basically, it is a time-consuming task. Emerging a new technique to identify the disease at an early stage increases the quality and efficacy of medicinal care. A state-of-the-art technologies like machine learning (ML) and artificial intelligence (AI) have been gradually being used to increase the efficacy and accuracy of CVD recognition, permitting for faster and more exact analysis, and finally contributing to superior management and prevention tactics for CV health. This research paper designs an Early Cardiovascular Disease Prediction using an Improved Beluga Whale Optimizer with Ensemble Learning (ECVDP-IBWOEL) approach via ECG Signal Analytics. The main intention of the ECVDP-IBWOEL system is to forecast the presence of CVD at the early stage using EEG signals. In the ECVDP-IBWOEL method, the primary phase of data preprocessing is initially implemented to convert the input data into a well-suited layout. Also, the ECVDP-IBWOEL technique follows an ensemble learning (EL) process for CVD detection comprising three models namely long short-term memory (LSTM), deep belief networks (DBNs), and stacked autoencoder (SAE). Finally, the IBWO algorithm-based hyperparameter tuning process takes place which can boost the classifier results of the ensemble models. To certify the enhanced results of the ECVDP-IBWOEL system, an extensive experimental study is made. The experimentation outcomes stated that the ECVDP-IBWOEL system underlines promising performance in the CVD prediction process