2026-04-17T05:24:34Zhttps://recercat.cat/oai/request

oai:recercat.cat:10256/129352024-06-13T09:51:15Zcom_2072_453036com_2072_2054col_2072_453038

00925njm 22002777a 4500 dc López Ibáñez, Beatriz author Viñas, Ramon author Torrent-Fontbona, Ferran author Fernández-Real Lemos, José Manuel author 2016 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) http://hdl.handle.net/10256/12935 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