IoT driven smart health monitoring for heart disease prediction using quantum kernel enhanced sardine diffusion and CNN

Heart disease is one of the major causes of death worldwide, and the traditional diagnostic procedures typically cause delays in treatment, particularly in low-resource regions. In this article, we propose a novel IoT-based Quantum Kernel-Enhanced Sardine Diffusion Attention Network (Qua-KSar-DCK-ArNet) for real-time prediction of heart disease. The system is capable of continuously monitoring heart-related data such as ECG and heart rate via IoT sensors. Quantum Clustering with k-Means is applied to cluster the data, and Z-score Min-Max Normalization is applied for preprocessing. Fast Point Transformer is utilized to identify salient features. The Qua-KSar-DCK-ArNet model, a combination of quantum and classical deep learning methods, classifies the data for predicting the risk of heart disease. The system is fast and accurate, with an accuracy of 99%, significantly improving patient outcomes, especially in resource-scarce regions.