IoT driven smart health monitoring for heart disease prediction using quantum kernel enhanced sardine diffusion and CNN.
Journal:
Scientific reports
PMID:
40389708
Abstract
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.