Heart health prediction and classification: An IoMT and AI collaborative model

¹ Computer Science and Engineering, Madanapalle Institute of Technology & Science, Kadiri Road, Angallu (V), Madanapalle - 517325, Annamayya District, Andhra Pradesh, India
² Department of Electronics and Communication Engineering, Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College), Tirupati - 517102, Andhra Pradesh, India
³ Department of computer science and Engineering, Hyderabad Institute of Technology and Management, Hyderabad, Telangana
⁴ Department of Computer Science and Engineering, IARE
⁵ Department of Computer Science and Engineering (Data Science), Vardhaman College of Engineering, Shamshabad, Hyderabad 501218
⁶ Rajeev Institute of Technology, Hassan
⁷ Department of CSE, GITAM Deemed to be University, Hyderabad, Rudararam, Sangareddy District- Telangana - 502329

^* Corresponding author: drsundarr@mits.ac.in

Abstract

Internet of Things (IoT) technology has been used in medical care as the Internet of Medical Things (IoMT) to gather sensor data for diagnosing and predicting cardiac disease. IoMT allows users to access real-time tracking information and manually estimate the person's health using Machine Learning (ML) algorithms. The primary goal of the study proposal is to categorize data and forecast heart illness using health information and medical imagery. The suggested IoMT-based Heart Health Prediction and Classification (IoMT-HHPC) model is a medical data categorization and forecasting framework in two phases. If the first stage's outcome effectively predicts heart disease, the second step is image classification. Data collected from medical equipment attached to the person's body were initially categorized. Echocardiography (ECG) images were analyzed to forecast cardiac problems. This article used many ML techniques to forecast cardiac disease. An IoMT-HHPC model with ANN achieved an accuracy of 99.02%, surpassing the performance of other ML algorithms.