dc.contributor.advisor | Bodenhofer, Ulrich | |
dc.contributor.author | Birklbauer, Micha Johannes | |
dc.date.accessioned | 2023-03-07T11:02:45Z | |
dc.date.available | 2023-03-07T11:02:45Z | |
dc.date.issued | 2019 | |
dc.date.submitted | 2019-09-03 | |
dc.identifier.uri | https://dspace.jcu.cz/handle/20.500.14390/40778 | |
dc.description.abstract | Imputation of missing data is a crucial step in data analysis since many statistical methods require complete datasets. In that regard MissForest imputation is a powerful tool that seems to outperform most other imputation approaches. This analysis evaluates how good imputation using MissForest is compared to other methods like imputation by Multivariate Imputation by Chained Equations (MICE), Restricted Boltzmann Machines (RBM) or the standard strawman (mean) imputation in a clinical dataset that is used to predict the mortality of patients after heart valve surgery. | cze |
dc.format | 40 p. (52855 characters) | |
dc.format | 40 p. (52855 characters) | |
dc.language.iso | xx | |
dc.publisher | Jihočeská univerzita | cze |
dc.rights | Bez omezení | |
dc.subject | imputation | cze |
dc.subject | missing data | cze |
dc.subject | missforest | cze |
dc.subject | mice | cze |
dc.subject | multivariate imputation by chained equations | cze |
dc.subject | rbm | cze |
dc.subject | restricted boltzmann machine | cze |
dc.subject | clinical data | cze |
dc.subject | machine learning | cze |
dc.subject | imputation | eng |
dc.subject | missing data | eng |
dc.subject | missforest | eng |
dc.subject | mice | eng |
dc.subject | multivariate imputation by chained equations | eng |
dc.subject | rbm | eng |
dc.subject | restricted boltzmann machine | eng |
dc.subject | clinical data | eng |
dc.subject | machine learning | eng |
dc.title | Imputation Of Missing Values In Clinical Data | cze |
dc.title.alternative | Imputation Of Missing Values In Clinical Data | eng |
dc.type | bakalářská práce | cze |
dc.identifier.stag | 55039 | |
dc.description.abstract-translated | Imputation of missing data is a crucial step in data analysis since many statistical methods require complete datasets. In that regard MissForest imputation is a powerful tool that seems to outperform most other imputation approaches. This analysis evaluates how good imputation using MissForest is compared to other methods like imputation by Multivariate Imputation by Chained Equations (MICE), Restricted Boltzmann Machines (RBM) or the standard strawman (mean) imputation in a clinical dataset that is used to predict the mortality of patients after heart valve surgery. | eng |
dc.date.accepted | 2019-09-09 | |
dc.description.department | Přírodovědecká fakulta | cze |
dc.thesis.degree-discipline | Bioinformatics | cze |
dc.thesis.degree-grantor | Jihočeská univerzita. Přírodovědecká fakulta | cze |
dc.thesis.degree-name | Bc. | |
dc.thesis.degree-program | Applied Informatics | cze |
dc.description.grade | Dokončená práce s úspěšnou obhajobou | cze |
dc.contributor.referee | Regl, Alois | |