An RHMIoT Framework for Cardiovascular Disease Prediction and Severity Level Using Machine Learning and Deep Learning Algorithms

Sibo Prasad Patro and Neelamadhab Padhy
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Sibo Prasad Patro: GIET University, India
Neelamadhab Padhy: GIET University, India

International Journal of Ambient Computing and Intelligence (IJACI), 2022, vol. 13, issue 1, 1-37

Abstract: Cardiovascular disease is one of the deadliest diseases in the world. Accurate analysis and prediction for real-time heart disease are highly significant. To address this challenge, a novel IoT-based automated function monitoring system to promote the e-healthcare system is proposed. The proposed remote healthcare monitoring system uses an IoT framework (RHMIoT) using deep learning and auto encoder-based machine learning algorithms to accurately predict the presence of heart disease. The RHMIoT framework contains two phases: the first phase is to monitor the severity level of the heart disease patient in real-time, and the second phase is used in the medical decision support system to predict the accuracy level of heart disease. To train and test the open-access Framingham dataset, various deep learning and auto encoder-based machine learning techniques are used. The proposed system obtains an accuracy of 0.8714% using the auto encoder-based kernel SVM algorithm compared to other approaches.

Date: 2022
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