Heart Sense: A novel IoT integrated Deep Learning Based ECG Image Analysis for Enhanced Heart Disease Prediction
Year: 2025
Type: article
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%.
Institution: Ghazi University
Language: English
Topic: ECG Monitoring and Analysis
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W7082989172
FWCI:
Cites:
Cited by:
Related to: