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Handling Missing Phenotype Data with Random Forests for Diabetes Risk Prognosis

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

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

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)

© 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

European Conference on Artificial Intelligence (ECAI)

Autor: López Ibáñez, Beatriz
Viñas, Ramon
Torrent-Fontbona, Ferran
Fernández-Real Lemos, José Manuel
Data: 2016
Resum: 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
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
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)
Format: application/pdf
Accés al document: http://hdl.handle.net/10256/12935
Llenguatge: eng
Editor: European Conference on Artificial Intelligence (ECAI)
Col·lecció: Versió postprint del document publicat a: http://www.ecai2016.org/content/uploads/2016/08/W7-AID-2016.pdf
Contribucions a congressos (D-EEEiA)
info:eu-repo/grantAgreement/EC/H2020/689810
És part de: © 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
Drets: Tots els drets reservats
Matèria: Diabetis no-insulinodependent
Non-insulin-dependent diabetes
Intel·ligència artificial -- Aplicacions a la medicina
Artificial intelligence -- Medical applications
Diabetis
Diabetes
Títol: Handling Missing Phenotype Data with Random Forests for Diabetes Risk Prognosis
Tipus: info:eu-repo/semantics/conferenceObject
Repositori: DUGiDocs

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