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Handling Missing Phenotype Data with Random Forests for Diabetes Risk Prognosis
López Ibáñez, Beatriz; Viñas, Ramon; Torrent-Fontbona, Ferran; Fernández-Real Lemos, José Manuel
Machine learning techniques are the cornerstone to handlethe amounts of information available for building comprehensivemodels for decision support in medical practice. However, thedatasets use to have a lot of missing information. In this work weanalyse how the random forests technique could be used for dealingwith missing phenotype values in order to prognosticate diabetestype 2
European Conference on Artificial Intelligence (ECAI), The Hage, Netherlands, 29 August - 2 September 2016 (Session 1st ECAI Workshop on Artificial Intelligence for Diabetes)
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)
Diabetis no-insulinodependent
Non-insulin-dependent diabetes
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European Conference on Artificial Intelligence (ECAI)
         

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