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Conference Paper: Bio-medical application on predicting systolic blood pressure using neural networks

TitleBio-medical application on predicting systolic blood pressure using neural networks
Authors
KeywordsSystolic blood pressure
Hypertension
Bio-medical big data application
Machine learning
Issue Date2015
PublisherIEEE Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1808984
Citation
The 2015 IEEE 1st International Conference on Big Data Computing Service and Applications (BigDataService), Redwood City, CA., 30 March-2 April 2015. In Conference Proceedings, 2015, p. 456-461 How to Cite?
AbstractThis paper presents a new study based on artificial neural network, which is a typical technique for processing big data, for the prediction of systolic blood pressure by correlated factors (gender, serum cholesterol, fasting blood sugar and electrocardiography signal). Two neural network algorithms, back-propagation neural network and radial basis function network, are used to construct and validate the bio-medical prediction system. The database of raw data is divided into two parts: 80% for training the neural network and the remaining 20% for testing the performance. The experimental result shows that artificial neural networks are suitable for modeling and predicting systolic blood pressure. This novel method of predicting systolic blood pressure contributes to giving early warnings to adults who may not take regular blood pressure measurements. Also, as it is known that an isolated blood pressure measurement is sometimes not very accurate due to the daily fluctuation, our predictor can provide another reference value to the medical staff.
Persistent Identifierhttp://hdl.handle.net/10722/214833
ISBN

 

DC FieldValueLanguage
dc.contributor.authorWu, TH-
dc.contributor.authorKwong, EWY-
dc.contributor.authorPang, GKH-
dc.date.accessioned2015-08-21T11:58:00Z-
dc.date.available2015-08-21T11:58:00Z-
dc.date.issued2015-
dc.identifier.citationThe 2015 IEEE 1st International Conference on Big Data Computing Service and Applications (BigDataService), Redwood City, CA., 30 March-2 April 2015. In Conference Proceedings, 2015, p. 456-461-
dc.identifier.isbn978-1-4799-8128-1-
dc.identifier.urihttp://hdl.handle.net/10722/214833-
dc.description.abstractThis paper presents a new study based on artificial neural network, which is a typical technique for processing big data, for the prediction of systolic blood pressure by correlated factors (gender, serum cholesterol, fasting blood sugar and electrocardiography signal). Two neural network algorithms, back-propagation neural network and radial basis function network, are used to construct and validate the bio-medical prediction system. The database of raw data is divided into two parts: 80% for training the neural network and the remaining 20% for testing the performance. The experimental result shows that artificial neural networks are suitable for modeling and predicting systolic blood pressure. This novel method of predicting systolic blood pressure contributes to giving early warnings to adults who may not take regular blood pressure measurements. Also, as it is known that an isolated blood pressure measurement is sometimes not very accurate due to the daily fluctuation, our predictor can provide another reference value to the medical staff.-
dc.languageeng-
dc.publisherIEEE Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1808984-
dc.relation.ispartofIEEE International Conference on Big Data Computing Service and Applications (BigDataService)-
dc.rightsIEEE International Conference on Big Data Computing Service and Applications (BigDataService). Copyright © IEEE Computer Society.-
dc.rights©2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectSystolic blood pressure-
dc.subjectHypertension-
dc.subjectBio-medical big data application-
dc.subjectMachine learning-
dc.titleBio-medical application on predicting systolic blood pressure using neural networks-
dc.typeConference_Paper-
dc.identifier.emailPang, GKH: gpang@eee.hku.hk-
dc.identifier.authorityPang, GKH=rp00162-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/BigDataService.2015.54-
dc.identifier.hkuros250194-
dc.identifier.spage456-
dc.identifier.epage461-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 151008-

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