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

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...