AUTHOR=Saikumar K. , Rajesh V. , Srivastava Gautam , Lin Jerry Chun-Wei 

TITLE=Heart disease detection based on internet of things data using linear quadratic discriminant analysis and a deep graph convolutional neural network

JOURNAL=Frontiers in Computational Neuroscience

VOLUME=Volume 16 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2022.964686

DOI=10.3389/fncom.2022.964686

ISSN=1662-5188

ABSTRACT=Heart diseases are emerging health issues in the medical field, according to WHO every year around 10 billion people are affected with heart abnormalities. Arteries in the heart have been generating oxygenated blood to all body parts, sometimes blood vessels are completely clogged or restrained the blood due to cardiac issues. Past heart diagnosis applications are outdated as well as performance is very less. Therefore, an intelligent heart disease diagnosis application design has been required. In this research work, IoT sensor data with a deep learning-based heart diagnosis application is designed on python 3.7.0 software. The Heart disease IoT sensor data is collected from the University of California Irvine machine learning repository free open-source dataset which is useful for training the DG_ConvoNet deep learning network. The testing data has been collected from Cleveland Clinic Foundation; it is a collection of 350 real-time clinical instances from heart patients through IoT sensors. The K-means technique is employed to remove noise in sensor data and clustered the unstructured data. The features are extracted to employ Linear Quadratic Discriminant Analysis, DG_ConvoNet is a deep learning process to classify and predict heart diseases. The diagnostic application accuracy of 96%, sensitivity of 80%, specificity of 73%, precision of 90%, F-Score of 79%, and area under the ROC curve of 75% are obtained by the proposed model.