An IoT Framework for Heart Disease Prediction based on MDCNN Classifier

TL;DR

This paper presents an IoT-based framework utilizing a Modified Deep Convolutional Neural Network (MDCNN) to improve the accuracy of heart disease prediction by analyzing sensor data from wearable devices.

Contribution

It introduces a novel MDCNN classifier integrated into an IoT framework for more accurate heart disease diagnosis from sensor data.

Findings

MDCNN outperforms existing neural networks and logistic regression.

Achieves 98.2% accuracy on maximum records.

Demonstrates improved diagnosis accuracy over traditional methods.

Abstract

Nowadays, heart disease is the leading cause of death worldwide. Predicting heart disease is a complex task since it requires experience along with advanced knowledge. Internet of Things (IoT) technology has lately been adopted in healthcare systems to collect sensor values for heart disease diagnosis and prediction. Many researchers have focused on the diagnosis of heart disease, yet the accuracy of the diagnosis results is low. To address this issue, an IoT framework is proposed to evaluate heart disease more accurately using a Modified Deep Convolutional Neural Network (MDCNN). The smartwatch and heart monitor device that is attached to the patient monitors the blood pressure and electrocardiogram (ECG). The MDCNN is utilized for classifying the received sensor data into normal and abnormal. The performance of the system is analyzed by comparing the proposed MDCNN with existing deep learning neural networks and logistic regression. The results demonstrate that the proposed MDCNN based heart disease prediction system performs better than other methods. The proposed method shows that for the maximum number of records, the MDCNN achieves an accuracy of 98.2 which is better than existing classifiers.