A deep learning approach for heart disease detection using a modified multiclass attention mechanism with BiLSTM

Heart disease remains the leading cause of death globally, mainly caused by delayed diagnosis and indeterminate categorization. Many of traditional ML/DL methods have limitations of misclassification, similar features, less training data, heavy computation, and noise disturbance. This study proposes a novel methodology of Modified Multiclass Attention Mechanism based on Deep Bidirectional Long Short-Term Memory (M2AM with Deep BiLSTM). We propose a novel model that incorporates class-aware attention weights, which dynamically modulate the focus of attention on input features according to their importance for a specific heart disease class. With an emphasis on the informative data, M2AM can improve feature representation and well-cure the problems of mis-classification, overlapped features, and fragility against noise. We utilized a large dataset of 6000 samples and 14 features, resulting in noticeable noise reduction from the MIT-BIH and INCART databases. Applying an Improved Adaptive band-pass filter (IABPF) to the signals resulted in noticeable noise reduction and an enhancement of signal quality. Additionally, wavelet transforms were employed to achieve accurate segmentation, allowing the model to discern the complex patterns present in the data. The proposed mechanism achieved high performance in the performance metrics, with accuracy of 98.82%, precision of 97.20%, recall of 98.34%, and F-measure of 98.92%. It surpassed methods such as the Classic Deep BiLSTM (SD-BiLSTM), and the standard approaches of Naive Bayes (NB), DNN-Taylos (DNNT), Multilayer perceptron (MLP-NN) and convolutional neural network (CNN). This work provides a solution to significant limitations of current methods and improves the accuracy of classification, indicating substantial progress in accurate diagnosis of heart diseases.