Two-Stage Classification Model for the Prediction of Heart Disease Using IoMT and Artificial Intelligence

Abstract

Internet of Things (IoT) technology has recently been applied in healthcare systems as an Internet of Medical Things (IoMT) to collect sensor information for the diagnosis and prognosis of heart disease. The main objective of the proposed research is to classify data and predict heart disease using medical data and medical images. The proposed model is a medical data classification and prediction model that operates in two stages. If the result from the first stage is efficient in predicting heart disease, there is no need for stage two. In the first stage, data gathered from medical sensors affixed to the patient’s body were classified; then, in stage two, echocardiogram image classification was performed for heart disease prediction. A hybrid linear discriminant analysis with the modified ant lion optimization (HLDA-MALO) technique was used for sensor data classification, while a hybrid Faster R-CNN with SE-ResNet-101 modelwass used for echocardiogram image classification. Both classification methods were carried out, and the classification findings were consolidated and validated to predict heart disease. The HLDA-MALO method obtained 96.85% accuracy in detecting normal sensor data, and 98.31% accuracy in detecting abnormal sensor data. The proposed hybrid Faster R-CNN with SE-ResNeXt-101 transfer learning model performed better in classifying echocardiogram images, with 98.06% precision, 98.95% recall, 96.32% specificity, a 99.02% F-score, and maximum accuracy of 99.15%.

An Accurate Heart Disease Prognosis Using Machine Intelligence and IoMT

“…The suitable attributes are described in Table 3. UCI database was used for echocardiography image retrieval using 66 normal images from 30 participants and 66 abnormal images from 30 subjects [4]. When the variables of "survival" and "still-alive" are combined together, it shows whether the patient has stayed alive at least one year after the heart attack or not.…”

“…Smart healthcare presents healthcare platforms which make use of tools such as IoT, wearable appliances, and wireless Internet connection for signing in health evidences and resource connection, organizations, and individuals. IoT, artificial intelligence (AI), big data, cloud networks, 5G, and advanced biotechnology are some of the smart healthcare networks used in disease screening and diagnosis and medical research [4].…”

A Deep Convolutional Neural Network for the Early Detection of Heart Disease

“…The precision, sensitivity, and specificity counts for the proposed work were 95.6%, 93.8% and 98.6%, respectively. The prediction of heart disease with DCNN using a method called CardioHelp was proposed by Awais et al [ 47 ]. The purpose of their study was the early detection of heart failure by utilizing a CNN model.…”

Medical Image Classification Using Transfer Learning and Chaos Game Optimization on the Internet of Medical Things

“…When used in the medical area, the IoT can construct care and monitoring systems that could be monitored remotely. It is now possible for medical professionals and sometimes even patients to remotely access sensor data generated by devices attached to persons who are being monitored or have specific requirements [ 23 ]. Computed-aided diagnosis (CAD) technologies can benefit from the IoT by providing an interaction that directly correlates the terminal to the devices for medical images' classification.…”