DUGiDocsHandling Missing Phenotype Data with Random Forests for Diabetes Risk Prognosis
dc.contributor.author
dc.date.accessioned
2016-10-25T12:06:06Z
dc.date.available
2016-10-25T12:06:06Z
dc.date.issued
2016
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dc.description
Comunicació de congrés presentada a: Workshop on Artificial Intelligence for Diabetes (AID) (1st: 2016: The Hague, Holanda) i European Conference on Artificial Intelligence (ECAI) (22nd: The Hage, Holanda)
Aquest workshop ha rebut finançament del programa d'investigació i innovació EU Horizon 2020 sota el núm. d'ajut 689810
dc.description.abstract
Machine learning techniques are the cornerstone to handle
the amounts of information available for building comprehensive
models for decision support in medical practice. However, the
datasets use to have a lot of missing information. In this work we
analyse how the random forests technique could be used for dealing
with missing phenotype values in order to prognosticate diabetes
type 2
dc.description.sponsorship
This project has received funding from the grant of the University of Girona 2016-2018 (MPCUdG2016) and the European Unions Horizon 2020 research and innovation programme under grant agreement No 689810 (PEPPER). The work has been developed with the support of the research group SITES awarded with distinction by the Generalitat de Catalunya (SGR 2014-2016)
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application/pdf
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eng
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European Conference on Artificial Intelligence (ECAI)
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Versió postprint del document publicat a: http://www.ecai2016.org/content/uploads/2016/08/W7-AID-2016.pdf
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© López, B., Herrero, P., Martin, C.(eds). (2016). AID: Artificial Intelligence for Diabetes: 1st ECAI Workshop on Artificial intelligence for Diabetes at the 22nd European Conference on Artificial Intelligence (ECAI 2016): 30 August 2016, The Hague, Holland: Proceedings, p. 39-42
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Contribucions a congressos (D-EEEiA)
dc.rights
Tots els drets reservats
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dc.title
Handling Missing Phenotype Data with Random Forests for Diabetes Risk Prognosis
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info:eu-repo/semantics/conferenceObject
dc.rights.accessRights
info:eu-repo/semantics/openAccess
dc.embargo.terms
Cap
dc.relation.projectID
info:eu-repo/grantAgreement/EC/H2020/689810/EU/Patient Empowerment through Predictive PERsonalised decision support/PEPPER
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info:eu-repo/semantics/acceptedVersion
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