2026-04-17T13:51:15Zhttps://recercat.cat/oai/requestoai:recercat.cat:10256/129352024-06-13T09:51:15Zcom_2072_453036com_2072_2054col_2072_453038
RECERCAT
author
López Ibáñez, Beatriz
author
Viñas, Ramon
author
Torrent-Fontbona, Ferran
author
Fernández-Real Lemos, José Manuel
2024-06-13T09:51:14Z
2024-06-13T09:51:14Z
2016
http://hdl.handle.net/10256/12935
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 689810Machine 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 2This 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)
Tots els drets reservats info:eu-repo/semantics/openAccess
Diabetis no-insulinodependent -- Congressos
Non-insulin-dependent diabetes -- Congresses
Intel·ligència artificial -- Aplicacions a la medicina -- Congressos
Artificial intelligence -- Medical applications -- Congresses
Diabetis -- Congressos
Diabetes -- Congresses
Handling Missing Phenotype Data with Random Forests for Diabetes Risk Prognosis
info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/acceptedVersion