DocumentCode :
2469146
Title :
Neural network based classifier for cardio vascular diseases based on vascular aging
Author :
Jayanthi, K B ; Banu, R. S. D. Wahida
Author_Institution :
Electronics and Communication Engineering Department, K.S. Rangasamy College of Technology, Tiruchengode-637215, Tamil Nadu, India
fYear :
2011
fDate :
Aug. 30 2011-Sept. 3 2011
Firstpage :
1109
Lastpage :
1112
Abstract :
Vascular Aging is a cardio vascular risk factor. Vascular aging and vascular disease go together. Cardio vascular diseases (CVDs) remain and will continue to be the leading cause of death in all countries. This rate is more, particularly in developing countries. This paper attempts to classify subjects tested as healthy or not based on the data obtained from the analysis of common carotid artery. The network taken for training and testing is a multilayer perceptron (MLP) with one hidden layer. Data obtained from the analysis has three parameters — diameter, distension and age of the subject under test. Subjects of varying age groups are taken for this. Network successfully classifies whether the person is ‘healthy’ or ‘should meet the cardiologist for further treatment’. Since this is done at a very early stage, this will be a milestone in the treatment of cardio vascular diseases. Moreover, this uses data obtained from the analysis of ultrasound images of the carotid artery and therefore is a cost effective method.
Keywords :
Aging; Cardiovascular diseases; Carotid arteries; Heart; Training; Aging; Carotid Artery Diseases; Carotid Artery, Common; Diagnosis, Computer-Assisted; Elasticity Imaging Techniques; Humans; Image Interpretation, Computer-Assisted; Neural Networks (Computer); Reproducibility of Results; Sensitivity and Specificity;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE
Conference_Location :
Boston, MA
ISSN :
1557-170X
Print_ISBN :
978-1-4244-4121-1
Electronic_ISBN :
1557-170X
Type :
conf
DOI :
10.1109/IEMBS.2011.6090259
Filename :
6090259
Link To Document :
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