Leveraging DNN-TransNet and Cloud Computing for Early Detection of Coronary Artery Disease

Coronary artery disease (CAD) is one of the major causes of death worldwide, and early diagnosis would ensure better patient management. Common CAD diagnostic methods are often tedious and require elaborate resources. This paper describes a hybrid model combining deep neural networks (DNN) with Transformer networks (DNN-TransNet) to facilitate cloud-based early detection of CAD. The model uses ECG sensor data integrated with clinical features to increase diagnostic accuracy, minimize processing time, and support scalability. Wavelet Transform is used for feature extraction from the ECG signal, thus enabling the analysis of timing and frequency. The integration of cloud computing enhances the scalability of such a system by online data processing access for medical practitioners. The synergy of these technologies is set to improve the accuracy of CAD detection and reduce delays in treatment for more personalized healthcare.