Evidence map›Paper›PMID 40389708›Full record

ArticleScientific reports2025

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

Dibas Kumar Hembram, Satya Narayan Tripathy, C H Narsimha Reddy, Debabrata Dansana, Quadri Noorulhasan Naveed, Shafat Khan, Mohammed Kareemullah, Addisu Frinjo Emma

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Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

8 authors.

Dibas Kumar HembramBerhampur University, Berhampur, Odisha, India. dkh.rs.cs@buodisha.edu.in.
Satya Narayan TripathyBerhampur University, Berhampur, Odisha, India.
C H Narsimha ReddyCSE-SOET, MNR University, Fasalwadi, Telangana, India.
Debabrata DansanaRajendra University, Balangir, Odisha, India.
Quadri Noorulhasan NaveedDepartment of Computer Science, College of Computer Science, King Khalid University, Abha, Saudi Arabia.
Shafat KhanDepartment of Computer Science, College of Computer Science, King Khalid University, Abha, Saudi Arabia.
Mohammed KareemullahDepartment of Mechanical Engineering, Graphic Era (Deemed to be University), Clement Town, Dehradun, 248002, India.
Addisu Frinjo EmmaCollege of Engineering and Technology, School of Mechanical and Automotive Engineering, Gedeo Zone, South Ethiopia Regional State, Dilla University, Po. Box 419, Dilla, Ethiopia. addisuf@du.edu.et.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Heart DiseasesInternet of ThingsNeural Networks, ComputerAlgorithmsDeep LearningElectrocardiographyHeart RateHumansMonitoring, PhysiologicDiffusion Kernel Attention NetworkHeart disease predictionQuantum-Classical Convolutional Neural NetworkSardine Optimization AlgorithmSmart health monitor

Identifiers

PMID40389708
PMCPMC12089537

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.