Heart Sense: A novel IoT integrated Deep Learning Based ECG Image Analysis forEnhanced Heart Disease Prediction

Rimsha Jamil Ghilzai ()
Additional contact information
Rimsha Jamil Ghilzai: Ghazi University D.G.Khan

International Journal of Innovations in Science & Technology, 2025, vol. 7, issue 1, 336-357

Abstract: The IoT based advancementsin the healthcare networks leveraging the unmatched capabilities of the Internet of Things for various fatal diseaseprediction and remote health monitoringthatproved to be very beneficial in providing timely and accurate healthcare services to patients.Patients whoare suffering from chronic diseases like blood pressure, kidney diseases,and heart diseases need treatment on time to avoid sudden deaths due to these ailments. To avoid this serious scenario,we have presented a novel approach for predicting heart diseases based on the Internet of Things.By leveraging the combined abilities of The IoT and Deep learning we have proposed an advanced approach thatwill able to predict heart diseases with increased accuracy and precision in comparison to theexisting approaches along with providing timely notifications to both patients and the medical professionals to deal with the situation at hand most effectively.We will be receiving real-time health data from the sensors which will be a wearable IoTdevice in our case. This collected data contains the continuously monitored information of the patient’sECG using an ECG sensing system that is sent to the cloud for precise disease prediction. We will also be employing the patients ‘electronic health records which will contain ECG images to increase the accuracy of our results. The Deep Learning model called the transformer will be used in the proposed approach for the precise prediction of cardiovascular disease in real-time. Both the healthcare professionals and the patients are provided with the relevant information if an ailment is predicted for effective healthcare monitoring and treatment. The proposed model has better results than the existing approaches for the prediction of heart disease in terms of accuracy which is 99.8%.

Keywords: Cardiovascular disease prediction; IoT; sensors; Deep learning; Transformer; ECG Images; Heart disease prediction (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journal.50sea.com/index.php/IJIST/article/view/1208/1733 (application/pdf)
https://journal.50sea.com/index.php/IJIST/article/view/1208 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:abq:ijist1:v:7:y:2025:i:1:p:336-357

Access Statistics for this article

International Journal of Innovations in Science & Technology is currently edited by Prof. Dr. Syed Amer Mahmood

More articles in International Journal of Innovations in Science & Technology from 50sea
Bibliographic data for series maintained by Iqra Nazeer ().