File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Conference Paper: Predicting systolic blood pressure using machine learning

TitlePredicting systolic blood pressure using machine learning
Authors
KeywordsArtificial neural network
Hypertension
Prediction
Systolic blood pressure
Issue Date2014
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001750
Citation
The 7th International Conference on Information and Automation for Sustainability (ICIAfS 2014), Colombo, Sri Lanka, 22-24 December 2014. In Conference Proceedings, 2014 How to Cite?
AbstractIn this paper, a new study based on machine learning technique, specifically artificial neural network, is investigated to predict the systolic blood pressure by correlated variables (BMI, age, exercise, alcohol, smoke level etc.). The raw data are split into two parts, 80% for training the machine and the remaining 20% for testing the performance. Two neural network algorithms, back-propagation neural network and radial basis function network, are used to construct and validate the prediction system. Based on a database with 498 people, the probabilities of the absolute difference between the measured and predicted value of systolic blood pressure under 10mm Hg are 51.9% for men and 52.5% for women using the back-propagation neural network With the same input variables and network status, the corresponding results based on the radial basis function network are 51.8% and 49.9% for men and women respectively. This novel method of predicting systolic blood pressure contributes to giving early warnings to young and middle-aged people who may not take regular blood pressure measurements. Also, as it is known 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. Our experimental result shows that artificial neural networks are suitable for modeling and predicting systolic blood pressure. © 2014 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/214837
ISBN

 

DC FieldValueLanguage
dc.contributor.authorWu, TH-
dc.contributor.authorPang, GKH-
dc.contributor.authorkwong, EWY-
dc.date.accessioned2015-08-21T11:58:07Z-
dc.date.available2015-08-21T11:58:07Z-
dc.date.issued2014-
dc.identifier.citationThe 7th International Conference on Information and Automation for Sustainability (ICIAfS 2014), Colombo, Sri Lanka, 22-24 December 2014. In Conference Proceedings, 2014-
dc.identifier.isbn978-1-4799-4598-6-
dc.identifier.urihttp://hdl.handle.net/10722/214837-
dc.description.abstractIn this paper, a new study based on machine learning technique, specifically artificial neural network, is investigated to predict the systolic blood pressure by correlated variables (BMI, age, exercise, alcohol, smoke level etc.). The raw data are split into two parts, 80% for training the machine and the remaining 20% for testing the performance. Two neural network algorithms, back-propagation neural network and radial basis function network, are used to construct and validate the prediction system. Based on a database with 498 people, the probabilities of the absolute difference between the measured and predicted value of systolic blood pressure under 10mm Hg are 51.9% for men and 52.5% for women using the back-propagation neural network With the same input variables and network status, the corresponding results based on the radial basis function network are 51.8% and 49.9% for men and women respectively. This novel method of predicting systolic blood pressure contributes to giving early warnings to young and middle-aged people who may not take regular blood pressure measurements. Also, as it is known 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. Our experimental result shows that artificial neural networks are suitable for modeling and predicting systolic blood pressure. © 2014 IEEE.-
dc.languageeng-
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001750-
dc.relation.ispartofInternational Conference on Information and Automation for Sustainability (ICIAfS)-
dc.rightsInternational Conference on Information and Automation for Sustainability (ICIAfS). Copyright © IEEE.-
dc.rights©2014 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.subjectArtificial neural network-
dc.subjectHypertension-
dc.subjectPrediction-
dc.subjectSystolic blood pressure-
dc.titlePredicting systolic blood pressure using machine learning-
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/ICIAFS.2014.7069529-
dc.identifier.scopuseid_2-s2.0-84927779175-
dc.identifier.hkuros250200-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 151008-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats