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

Rimsha Jamil Ghilzai; Urwa Bibi; Hafiz Gulfam Ahmad Umer; Sana Rubab

Authors

Rimsha Jamil Ghilzai Ghazi University D.G.Khan
Urwa Bibi Ghazi University D.G.Khan
Hafiz Gulfam Ahmad Umer Ghazi University D.G.Khan
Sana Rubab Ghazi University D.G.Khan

Keywords:

Cardiovascular Disease Prediction, IOT, sensors, Deep Learning, Transformer, ECG Images, Heart disease prediction

Abstract

The increasing advancements in the healthcare networks leveraging the unmatched capabilities of the Internet of Things for various fatal disease prediction and remote health monitoring that proved to be very beneficial in providing timely and accurate healthcare services to patients. Patients who are 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 that will able to predict heart diseases with increased accuracy and precision in comparison to the existing 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 IoT device in our case. This collected data contains the continuously monitored information of the patient’s ECG 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%.

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