A Robust Heart Disease Prediction System Using Hybrid Deep Neural Networks

Heart Disease (HD) is recognized as the leading cause of worldwide mortality by the World Health Organization (WHO), resulting in the loss of approximately 17.9 million lives each year. HD prediction is found to be a challenging issue that can provide a computerized estimate of the level of HD so that additional action can be simplified. Early detection and accurate prediction of HD play a critical role in providing timely medical interventions and improving patient outcomes. Thus, HD prediction has expected massive attention worldwide in healthcare environments. Deep Learning (DL) based systems played a significant role in various disease prediction and diagnosis with good efficiency. To this end, the main contribution of this paper is to design a robust HD prediction system using Hybrid Deep Neural Networks (HDNNs) involves combining multiple neural network architectures to extract and learn relevant features from the input data. The HDNN is employed to apply its feature learning capabilities and non-linear technology to capture complex patterns and relationships in HD datasets, leading to enhanced prediction accuracy. For this, three DL models, namely Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and a new HDNN model combining both CNN and LSTM along with additional Dense layers are proposed, to develop the hybrid HD prediction architecture. The proposed models were evaluated on two publicly available HD datasets, including the Cleveland HD dataset, and a large public HD dataset (Switzerland + Cleveland + Statlog + Hungarian + Long Beach VA). Additionally, the proposed system was measured through comparison with conventional systems concerning sensitivity, Matthews Correlation Coefficient (MCC), F1-measure, accuracy, precision, AUC, and specificity. The promising accuracy achieved through the proposed system is 98.86%. The results demonstrated that this approach proved more accurate in its predictions than previous research. These outcomes suggest that the proposed HDNN system has great potential to be embedded into healthcare systems to develop advanced and reliable HD prediction models that can significantly contribute to medical diagnosis and improve patient care.