The Ensembles of Machine Learning Methods for Survival Predicting after Kidney Transplantation

Machine learning is used to develop predictive models to diagnose different diseases, particularly kidney transplant survival prediction. The paper used the collected dataset of patients’ individual parameters to predict the critical risk factors associated with early graft rejection. Our study show...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Yaroslav Tolstyak, Rostyslav Zhuk, Igor Yakovlev, Nataliya Shakhovska, Michal Gregus ml, Valentyna Chopyak, Nataliia Melnykova
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	organ transplantation Kapplan-Meier method machine learning ensemble early risk prediction Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/a8b1de2a13dc480cba6a74e66d0074a0
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:a8b1de2a13dc480cba6a74e66d0074a0
record_format	dspace
spelling	oai:doaj.org-article:a8b1de2a13dc480cba6a74e66d0074a02021-11-11T15:23:53ZThe Ensembles of Machine Learning Methods for Survival Predicting after Kidney Transplantation10.3390/app1121103802076-3417https://doaj.org/article/a8b1de2a13dc480cba6a74e66d0074a02021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10380https://doaj.org/toc/2076-3417Machine learning is used to develop predictive models to diagnose different diseases, particularly kidney transplant survival prediction. The paper used the collected dataset of patients’ individual parameters to predict the critical risk factors associated with early graft rejection. Our study shows the high pairwise correlation between a massive subset of the parameters listed in the dataset. Hence the proper feature selection is needed to increase the quality of a prediction model. Several methods are used for feature selection, and results are summarized using hard voting. Modeling the onset of critical events for the elements of a particular set is made based on the Kapplan-Meier method. Four novel ensembles of machine learning models are built on selected features for the classification task. Proposed stacking allows obtaining an accuracy, sensitivity, and specifity of more than 0.9. Further research will include the development of a two-stage predictor.Yaroslav TolstyakRostyslav ZhukIgor YakovlevNataliya ShakhovskaMichal Gregus mlValentyna ChopyakNataliia MelnykovaMDPI AGarticleorgan transplantationKapplan-Meier methodmachine learningensembleearly risk predictionTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10380, p 10380 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	organ transplantation Kapplan-Meier method machine learning ensemble early risk prediction Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	organ transplantation Kapplan-Meier method machine learning ensemble early risk prediction Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Yaroslav Tolstyak Rostyslav Zhuk Igor Yakovlev Nataliya Shakhovska Michal Gregus ml Valentyna Chopyak Nataliia Melnykova The Ensembles of Machine Learning Methods for Survival Predicting after Kidney Transplantation
description	Machine learning is used to develop predictive models to diagnose different diseases, particularly kidney transplant survival prediction. The paper used the collected dataset of patients’ individual parameters to predict the critical risk factors associated with early graft rejection. Our study shows the high pairwise correlation between a massive subset of the parameters listed in the dataset. Hence the proper feature selection is needed to increase the quality of a prediction model. Several methods are used for feature selection, and results are summarized using hard voting. Modeling the onset of critical events for the elements of a particular set is made based on the Kapplan-Meier method. Four novel ensembles of machine learning models are built on selected features for the classification task. Proposed stacking allows obtaining an accuracy, sensitivity, and specifity of more than 0.9. Further research will include the development of a two-stage predictor.
format	article
author	Yaroslav Tolstyak Rostyslav Zhuk Igor Yakovlev Nataliya Shakhovska Michal Gregus ml Valentyna Chopyak Nataliia Melnykova
author_facet	Yaroslav Tolstyak Rostyslav Zhuk Igor Yakovlev Nataliya Shakhovska Michal Gregus ml Valentyna Chopyak Nataliia Melnykova
author_sort	Yaroslav Tolstyak
title	The Ensembles of Machine Learning Methods for Survival Predicting after Kidney Transplantation
title_short	The Ensembles of Machine Learning Methods for Survival Predicting after Kidney Transplantation
title_full	The Ensembles of Machine Learning Methods for Survival Predicting after Kidney Transplantation
title_fullStr	The Ensembles of Machine Learning Methods for Survival Predicting after Kidney Transplantation
title_full_unstemmed	The Ensembles of Machine Learning Methods for Survival Predicting after Kidney Transplantation
title_sort	ensembles of machine learning methods for survival predicting after kidney transplantation
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/a8b1de2a13dc480cba6a74e66d0074a0
work_keys_str_mv	AT yaroslavtolstyak theensemblesofmachinelearningmethodsforsurvivalpredictingafterkidneytransplantation AT rostyslavzhuk theensemblesofmachinelearningmethodsforsurvivalpredictingafterkidneytransplantation AT igoryakovlev theensemblesofmachinelearningmethodsforsurvivalpredictingafterkidneytransplantation AT nataliyashakhovska theensemblesofmachinelearningmethodsforsurvivalpredictingafterkidneytransplantation AT michalgregusml theensemblesofmachinelearningmethodsforsurvivalpredictingafterkidneytransplantation AT valentynachopyak theensemblesofmachinelearningmethodsforsurvivalpredictingafterkidneytransplantation AT nataliiamelnykova theensemblesofmachinelearningmethodsforsurvivalpredictingafterkidneytransplantation AT yaroslavtolstyak ensemblesofmachinelearningmethodsforsurvivalpredictingafterkidneytransplantation AT rostyslavzhuk ensemblesofmachinelearningmethodsforsurvivalpredictingafterkidneytransplantation AT igoryakovlev ensemblesofmachinelearningmethodsforsurvivalpredictingafterkidneytransplantation AT nataliyashakhovska ensemblesofmachinelearningmethodsforsurvivalpredictingafterkidneytransplantation AT michalgregusml ensemblesofmachinelearningmethodsforsurvivalpredictingafterkidneytransplantation AT valentynachopyak ensemblesofmachinelearningmethodsforsurvivalpredictingafterkidneytransplantation AT nataliiamelnykova ensemblesofmachinelearningmethodsforsurvivalpredictingafterkidneytransplantation
_version_	1718435383315791872

The Ensembles of Machine Learning Methods for Survival Predicting after Kidney Transplantation

Ejemplares similares