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Article: A Prediction Model of Blood Pressure for Telemedicine

TitleA Prediction Model of Blood Pressure for Telemedicine
Authors
Issue Date2016
PublisherSage Publications Ltd. The Journal's web site is located at http://www.sagepub.co.uk/journal.aspx?pid=105571
Citation
Health Informatics Journal, 2016 How to Cite?
AbstractThis paper presents a new study based on a machine learning technique, specifically an artificial neural network, for predicting systolic blood pressure through the correlation of variables (age, BMI, exercise level, alcohol consumption level, smoking status, stress level, and salt intake level). The study was carried out using a database containing a variety of variables/factors. Each database of raw data was split into two parts: one part for training the neural network and the remaining part for testing the performance of the network. Two neural network algorithms, back-propagation and radial basis function, were used to construct and validate the prediction system. According to the experiment, the accuracy of our predictions of systolic blood pressure values exceeded 90%. Our experimental results show that artificial neural networks are suitable for modeling and predicting systolic blood pressure. This new method of predicting systolic blood pressure helps to give an early warning to adults, who may not get regular blood pressure measurements that their blood pressure might be at an unhealthy level. Also, because an isolated measurement of blood pressure is not always very accurate due to daily fluctuations, our predictor can provide the predicted value as another figure for medical staff to refer to.
Persistent Identifierhttp://hdl.handle.net/10722/229175
ISSN
2015 Impact Factor: 1.578
2015 SCImago Journal Rankings: 0.505

 

DC FieldValueLanguage
dc.contributor.authorKwong, EWY-
dc.contributor.authorWu, H-
dc.contributor.authorPang, GKH-
dc.date.accessioned2016-08-23T14:09:28Z-
dc.date.available2016-08-23T14:09:28Z-
dc.date.issued2016-
dc.identifier.citationHealth Informatics Journal, 2016-
dc.identifier.issn1460-4582-
dc.identifier.urihttp://hdl.handle.net/10722/229175-
dc.description.abstractThis paper presents a new study based on a machine learning technique, specifically an artificial neural network, for predicting systolic blood pressure through the correlation of variables (age, BMI, exercise level, alcohol consumption level, smoking status, stress level, and salt intake level). The study was carried out using a database containing a variety of variables/factors. Each database of raw data was split into two parts: one part for training the neural network and the remaining part for testing the performance of the network. Two neural network algorithms, back-propagation and radial basis function, were used to construct and validate the prediction system. According to the experiment, the accuracy of our predictions of systolic blood pressure values exceeded 90%. Our experimental results show that artificial neural networks are suitable for modeling and predicting systolic blood pressure. This new method of predicting systolic blood pressure helps to give an early warning to adults, who may not get regular blood pressure measurements that their blood pressure might be at an unhealthy level. Also, because an isolated measurement of blood pressure is not always very accurate due to daily fluctuations, our predictor can provide the predicted value as another figure for medical staff to refer to.-
dc.languageeng-
dc.publisherSage Publications Ltd. The Journal's web site is located at http://www.sagepub.co.uk/journal.aspx?pid=105571-
dc.relation.ispartofHealth Informatics Journal-
dc.rightsHealth Informatics Journal. Copyright © Sage Publications Ltd.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleA Prediction Model of Blood Pressure for Telemedicine-
dc.typeArticle-
dc.identifier.emailPang, GKH: gpang@eee.hku.hk-
dc.identifier.authorityPang, GKH=rp00162-
dc.description.naturepostprint-
dc.identifier.doi10.1177/1460458216663025-
dc.identifier.pmid27496863-
dc.identifier.hkuros259914-
dc.publisher.placeUnited Kingdom-

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