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

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

Sibo Prasad Patro, Neelamadhab Padhy
Copyright: © 2022 |Volume: 13 |Issue: 1 |Pages: 37
ISSN: 1941-6237|EISSN: 1941-6245|EISBN13: 9781683180647|DOI: 10.4018/IJACI.311062
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MLA

Patro, Sibo Prasad, and Neelamadhab Padhy. "An RHMIoT Framework for Cardiovascular Disease Prediction and Severity Level Using Machine Learning and Deep Learning Algorithms." IJACI vol.13, no.1 2022: pp.1-37. http://doi.org/10.4018/IJACI.311062

APA

Patro, S. P. & Padhy, N. (2022). An RHMIoT Framework for Cardiovascular Disease Prediction and Severity Level Using Machine Learning and Deep Learning Algorithms. International Journal of Ambient Computing and Intelligence (IJACI), 13(1), 1-37. http://doi.org/10.4018/IJACI.311062

Chicago

Patro, Sibo Prasad, and Neelamadhab Padhy. "An RHMIoT Framework for Cardiovascular Disease Prediction and Severity Level Using Machine Learning and Deep Learning Algorithms," International Journal of Ambient Computing and Intelligence (IJACI) 13, no.1: 1-37. http://doi.org/10.4018/IJACI.311062

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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.

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