  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.
