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

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 2; p. 476
• Main Authors: Manimurugan, S., Almutairi, Saad, Aborokbah, Majed Mohammed, Narmatha, C., Ganesan, Subramaniam, Chilamkurti, Naveen, Alzaheb, Riyadh A., Almoamari, Hani
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.01.2022; MDPI
• Subjects: Artificial Intelligence; Cardiovascular disease; Classification; cloud; Decision making; Deep learning; Delivery of Health Care; Discriminant analysis; Feature selection; Heart; heart disease prediction; Heart diseases; Heart Diseases - diagnostic imaging; Humans; hybrid Faster R-CNN with SE-ResNet-101; hybrid linear discriminant analysis with modified ant lion optimization; Information management; Internet; Internet of Medical Things; Internet of Things; Medical advice systems; Medical equipment; medical image; Medical research; Medicine, Experimental; Mobile communications networks; Neural networks; Optimization techniques; Patients; Prognosis; Sensors; Telemedicine; Ultrasonic imaging; Saudi Arabia; Ohio; cloud; medical image; hybrid Faster R-CNN with SE-ResNet-101; hybrid linear discriminant analysis with modified ant lion optimization; heart disease prediction; Internet of Medical Things
• DOI: 10.3390/s22020476
• ISSN: 1424-8220

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%.