An Optimized Progressive Attention-Based Bidirectional Encoder Enclosed Transformer Network-Based Cardiovascular Disease Detection

Cardiovascular disease (CVD) diagnosis often faces challenges due to poor data quality, noisy signals and ineffective feature extraction from clinical and electrocardiogram (ECG) data. The processing methods currently used for diagnosing time-series data containing outliers produce imprecise results as they do not handle high dimensions effectively. This study proposes a modern CVD diagnostic system through advanced implementation of pre- processing methods, extraction algorithms, and model optimization tools. To guarantee data quality and consistency, clinical data and ECG signals were first pre-processed using “missing value imputation, one-hot encoding, and z-score normalization”. ECG signals were further denoised using a “Finite Impulse Response (FIR) filter” to eliminate unwanted noise and artifacts. Feature extraction from pre-processed clinical data was performed using a “Dense-Assisted Parallel Attention-Based MobileNet (DPAM)” model, which efficiently captured intricate patterns in the data. For ECG data, features were derived using “Legendre Multi-Wavelet Transform-Based Feature Decomposition (LMT-FD)” model, enabling effective signal decomposition into meaningful components. A complete dataset was produced by fusing the characteristics that were collected from clinical and ECG data. “Optimized Progressive Attention-Based Bidirectional Encoder Enclosed Transformer Network (OPA-BETN)” was used for CVD diagnosis. The “Leaf in Wind Optimization (LWO)” approach was used for hyperparameter tuning of the model in order to maximize its performance. The PTB-XL dataset, which included clinical and ECG signal data, was used in this study. Till feature extraction, data underwent individual processing. Following fusion, the features from both data types were integrated. For improved analysis, two characteristics, heart rate and caloric intake, were included in the clinical data from Apple Watch Fitbit dataset. The experimental results showed that the PTB-XL dataset attained higher accuracy, precision, recall, F1-Score, Specificity, TPR, TNR, FPR and FNR values of 99.0%, 97.2%, 97.2%, 97.2%, 99.3%, 97.2, 99.3, 0.006 and 0.027, respectively.