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Investigation of th...
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Tedesco, SalvatoreUniversity College Cork, Tyndall National Institute, Cork, Ireland
(author)
Investigation of the analysis of wearable data for cancer-specific mortality prediction in older adults
- Article/chapterEnglish2021
Publisher, publication year, extent ...
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IEEE,2021
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Numbers
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LIBRIS-ID:oai:DiVA.org:umu-202939
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https://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-202939URI
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https://doi.org/10.1109/EMBC46164.2021.9630370DOI
Supplementary language notes
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Language:English
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Summary in:English
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Subject category:ref swepub-contenttype
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Subject category:kon swepub-publicationtype
Notes
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Cancer is an aggressive disease which imparts a tremendous socio-economic burden on the international community. Early detection is an important aspect in improving survival rates for cancer sufferers; however, very few studies have investigated the possibility of predicting which people have the highest risk to develop this disease, even years before the traditional symptoms first occur. In this paper, a dataset from a longitudinal study which was collected among 2291 70-year olds in Sweden has been analyzed to investigate the possibility for predicting 2-7 year cancer-specific mortality. A tailored ensemble model has been developed to tackle this highly imbalanced dataset. The performance with different feature subsets has been investigated to evaluate the impact that heterogeneous data sources may have on the overall model. While a full-features model shows an Area Under the ROC Curve (AUC-ROC) of 0.882, a feature subset which only includes demographics, self-report health and lifestyle data, and wearable dataset collected in free-living environments presents similar performance (AUC-ROC: 0.857). This analysis confirms the importance of wearable technology for providing unbiased health markers and suggests its possible use in the accurate prediction of 2-7 year cancer-related mortality in older adults.
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Andrulli, MartinaUniversity College Cork, Tyndall National Institute, Cork, Ireland
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Åkerlund Larsson, MarkusUmeå universitet,Institutionen för folkhälsa och klinisk medicin(Swepub:umu)maak0017
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Kelly, DanielSchool of Computing, Engineering and Intelligent Systems, Ulster University, Londonderry, United Kingdom
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Timmons, SuzanneCentre for Gerontology and Rehabilitation, University College Cork, Cork, Ireland
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Alamäki, AnttiDepartment of Physiotherapy, Karelia University of Applied Sciences, Joensuu, Finland
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Barton, JohnUniversity College Cork, Tyndall National Institute, Cork, Ireland
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Condell, JoanSchool of Computing, Engineering and Intelligent Systems, Ulster University, Londonderry, United Kingdom
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O'Flynn, BrendanUniversity College Cork, Tyndall National Institute, Cork, Ireland
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Nordström, AnnaUmeå universitet,Institutionen för folkhälsa och klinisk medicin,School of Sport Sciences, UiT The Arctic University of Norway, Tromsø, Norway(Swepub:umu)anagun97
(author)
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University College Cork, Tyndall National Institute, Cork, IrelandInstitutionen för folkhälsa och klinisk medicin
(creator_code:org_t)
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In:Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS: IEEE, s. 1848-18519781728111797
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