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dc.contributor.authorEffiok, Emmanuel
dc.contributor.authorLiu, Enjie
dc.contributor.authorHitchcock, Jonathan James
dc.date.accessioned2022-03-24T10:41:13Z
dc.date.available2022-03-17T00:00:00Z
dc.date.available2022-03-24T10:41:13Z
dc.date.issued2022-03-17
dc.identifier.citationEffiok E, Liu E, Hitchcock J (2022) 'Predicting cumulative effect of lifestyle risk factors for complex disease', IET Healthcare Technology Letters, 9 (3), pp.34-42.en_US
dc.identifier.issn2053-3713
dc.identifier.doi10.1049/htl2.12021
dc.identifier.urihttp://hdl.handle.net/10547/625357
dc.description.abstractIn medical domain, risk factors are often used to model disease predictions. In order to make the most use of the predictive models, linking the model with real patient data generates personalized disease progression and predictions. However, the risk factors are fragmented all over medical literature, certain risks can be accumulated for a disease and the aggregated probability may increase or decrease the occurrence of a disease. In this paper, we propose a risk predictive framework which forms a base for a complete risk prediction model that can be used for various health applications.en_US
dc.language.isoenen_US
dc.publisherIET/Wileyen_US
dc.relation.urlhttps://ietresearch.onlinelibrary.wiley.com/doi/10.1049/htl2.12021en_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectrisk predictive modelsen_US
dc.subjectrisk factoren_US
dc.subjectdisease risk predictive modellingen_US
dc.subjectSubject Categories::H123 Public Health Engineeringen_US
dc.titlePredicting cumulative effect of lifestyle risk factors for complex diseaseen_US
dc.typeArticleen_US
dc.contributor.departmentUniversity of Bedfordshireen_US
dc.identifier.journalIET Healthcare Technology Lettersen_US
dc.date.updated2022-03-24T10:37:36Z
dc.description.notegold open access


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Attribution-NonCommercial-NoDerivatives 4.0 International
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 International