Optimized CNN-BiLSTM with Attention: A High Performance Model for Predicting Heart Disease Using Cleveland and Framingham Datasets

Worldwide, some 17.9 million survives are lost each year due to heart disease (HD), which is acknowledged by the World Health Organisation (WHO) as top cause of mortality. In order to simplify further action, HD prediction—a difficult problem—can give a computerised estimate of the HD level. Improving patient outcomes and allowing for timely medical interventions are both made possible by early detection and accurate calculation of HD. As a result, HD prediction has garnered a great deal of interest from healthcare facilities around the globe. There has been encouraging progress in the detection of cardiac illness thanks to recent developments in machine learning (ML). Transparency and explainability, in addition to generalisability and robustness, are crucial for ML models to be used in therapeutic settings. The efficient prediction and diagnosis of numerous diseases was greatly aided by systems based on Deep Learning (DL). By combining Convolutional Neural Networks (CNNs), Bidirectional Long Short-Term Memory (BiLSTMs), besides Attention Mechanisms (CNN-AM), this paper aims to build a strong HD prediction scheme. Minimal preparation is necessary for this procedure. To extract spatial features, CNN is used. To extract temporal characteristics, Bi-LSTM is used. Lastly, to filter out the outcomes of the more to ighted channel output classification, two channel to ights are allotted through the attention mechanism. The proposed model's parameters are fine-tuned using a new optimisation approach known as Newton-Raphson-based Optimiser (NRO), which ultimately leads to better classification accuracy. With accuracy of 95.3% on the Cleveland dataset and 98.1% on the Framingham dataset, respectively, the optimised CNN-BiLSTM-AM model demonstrated the best performance in the experimental findings.