Utilization of Artificial Intelligence-Based Wearable Sensors in Deep Residual Network for Detecting Heart Disease

Abstract

Recently, there has been growing attention to the advances in the areas of electronic and biomedical engineering and the great applications that these technologies can offer mainly for health diagnosis and monitoring. In the past decade, deep learning (DL) has revolutionized traditional machine learning (ML) and brought about improved performance in many fields, including image recognition, object detection, speech recognition, and natural language processing. This chapter discusses detection of heart disease using deep learning techniques. Here the input data has been collected based on wearable device-collected data with IoT module. This data has been preprocessed using adaptive histogram normalization, and the authors segment the image based on threshold method using Ostu thresholding technique. The segmented image feature has been extracted using generative adversarial network and classification of extracted features using deep residual network. The experimental analysis is obtained by the proposed GAN_DRN in terms of accuracy as 96%, precision of 85%, recall of 80%, F-1 score of 71%, and AUC of 75%.