Comparison of Ordinal Response Modeling Methods like Decision Trees, Ordinal Forest and L1 Penalized Continuation Ratio Regression in High Dimensional Data

Background: Response variables in most medical and health-related research have an ordinal nature. Conventional modeling methods assume predictor variables to be independent, and consider a large number of samples (n) compared to the number of covariates (p). Therefore, it is not possible to use con...

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Autores principales:	Zahra Torkashvand, Hossein Mahjub, Ali Reza Soltanian, Maryam Farhadian
Formato:	article
Lenguaje:	EN FA
Publicado:	Bushehr University of Medical Sciences 2021
Materias:	ordinal response ordinal forest l1 penalized continuation ratio regression high dimensional data Medicine (General) R5-920
Acceso en línea:	https://doaj.org/article/97dc132614874af190a02b892c7f7d96
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spelling	oai:doaj.org-article:97dc132614874af190a02b892c7f7d962021-12-04T05:40:39ZComparison of Ordinal Response Modeling Methods like Decision Trees, Ordinal Forest and L1 Penalized Continuation Ratio Regression in High Dimensional Data1735-43741735-6954https://doaj.org/article/97dc132614874af190a02b892c7f7d962021-11-01T00:00:00Zhttp://ismj.bpums.ac.ir/article-1-1515-en.htmlhttps://doaj.org/toc/1735-4374https://doaj.org/toc/1735-6954Background: Response variables in most medical and health-related research have an ordinal nature. Conventional modeling methods assume predictor variables to be independent, and consider a large number of samples (n) compared to the number of covariates (p). Therefore, it is not possible to use conventional models for high dimensional genetic data in which p > n. The present study compared the predictive performance of decision trees, ordinal forest, and L1 penalized continuation ratio regression. Materials and Methods: In the present study, three data sets were used. The B-cell data contained 12,625 gene expression data related to 128 patients with four ordinal levels of response variables. The HCC data related to liver cancer included 1469 genes of 56 patients with three ordinal levels of response variables. The Heart data contained information of five variables in 294 patients undergoing angiography with five ordinal levels of response variables. The performance of the methods was compared based on the same training and test datasets using indicators such as accuracy, gamma, and kappa. Results: For two high-dimensional data sets, the ordinal forest model had a higher predictive ability while for the low-dimensional data set, the L1 penalized continuation ratio model had a better predictive performance. Conclusion: The selection of the best prediction model depends on the data set, and for each data, different methods should be considered to achieve the best model.Zahra TorkashvandHossein MahjubAli Reza SoltanianMaryam FarhadianBushehr University of Medical Sciencesarticleordinal responseordinal forestl1 penalized continuation ratio regressionhigh dimensional dataMedicine (General)R5-920ENFAIranian South Medical Journal , Vol 24, Iss 5, Pp 454-468 (2021)
institution	DOAJ
collection	DOAJ
language	EN FA
topic	ordinal response ordinal forest l1 penalized continuation ratio regression high dimensional data Medicine (General) R5-920
spellingShingle	ordinal response ordinal forest l1 penalized continuation ratio regression high dimensional data Medicine (General) R5-920 Zahra Torkashvand Hossein Mahjub Ali Reza Soltanian Maryam Farhadian Comparison of Ordinal Response Modeling Methods like Decision Trees, Ordinal Forest and L1 Penalized Continuation Ratio Regression in High Dimensional Data
description	Background: Response variables in most medical and health-related research have an ordinal nature. Conventional modeling methods assume predictor variables to be independent, and consider a large number of samples (n) compared to the number of covariates (p). Therefore, it is not possible to use conventional models for high dimensional genetic data in which p > n. The present study compared the predictive performance of decision trees, ordinal forest, and L1 penalized continuation ratio regression. Materials and Methods: In the present study, three data sets were used. The B-cell data contained 12,625 gene expression data related to 128 patients with four ordinal levels of response variables. The HCC data related to liver cancer included 1469 genes of 56 patients with three ordinal levels of response variables. The Heart data contained information of five variables in 294 patients undergoing angiography with five ordinal levels of response variables. The performance of the methods was compared based on the same training and test datasets using indicators such as accuracy, gamma, and kappa. Results: For two high-dimensional data sets, the ordinal forest model had a higher predictive ability while for the low-dimensional data set, the L1 penalized continuation ratio model had a better predictive performance. Conclusion: The selection of the best prediction model depends on the data set, and for each data, different methods should be considered to achieve the best model.
format	article
author	Zahra Torkashvand Hossein Mahjub Ali Reza Soltanian Maryam Farhadian
author_facet	Zahra Torkashvand Hossein Mahjub Ali Reza Soltanian Maryam Farhadian
author_sort	Zahra Torkashvand
title	Comparison of Ordinal Response Modeling Methods like Decision Trees, Ordinal Forest and L1 Penalized Continuation Ratio Regression in High Dimensional Data
title_short	Comparison of Ordinal Response Modeling Methods like Decision Trees, Ordinal Forest and L1 Penalized Continuation Ratio Regression in High Dimensional Data
title_full	Comparison of Ordinal Response Modeling Methods like Decision Trees, Ordinal Forest and L1 Penalized Continuation Ratio Regression in High Dimensional Data
title_fullStr	Comparison of Ordinal Response Modeling Methods like Decision Trees, Ordinal Forest and L1 Penalized Continuation Ratio Regression in High Dimensional Data
title_full_unstemmed	Comparison of Ordinal Response Modeling Methods like Decision Trees, Ordinal Forest and L1 Penalized Continuation Ratio Regression in High Dimensional Data
title_sort	comparison of ordinal response modeling methods like decision trees, ordinal forest and l1 penalized continuation ratio regression in high dimensional data
publisher	Bushehr University of Medical Sciences
publishDate	2021
url	https://doaj.org/article/97dc132614874af190a02b892c7f7d96
work_keys_str_mv	AT zahratorkashvand comparisonofordinalresponsemodelingmethodslikedecisiontreesordinalforestandl1penalizedcontinuationratioregressioninhighdimensionaldata AT hosseinmahjub comparisonofordinalresponsemodelingmethodslikedecisiontreesordinalforestandl1penalizedcontinuationratioregressioninhighdimensionaldata AT alirezasoltanian comparisonofordinalresponsemodelingmethodslikedecisiontreesordinalforestandl1penalizedcontinuationratioregressioninhighdimensionaldata AT maryamfarhadian comparisonofordinalresponsemodelingmethodslikedecisiontreesordinalforestandl1penalizedcontinuationratioregressioninhighdimensionaldata
_version_	1718372810093494272

Comparison of Ordinal Response Modeling Methods like Decision Trees, Ordinal Forest and L1 Penalized Continuation Ratio Regression in High Dimensional Data

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