Mixture of Survival Analysis Models-Cluster-Weighted Weibull Distributions

Survival analysis is a widely used method to establish a connection between a time to event outcome and a set of variables. The goal of this work is to improve the accuracy of the widely applied parametric survival models. This work highlights that accurate and interpretable survival analysis models...

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Autores principales:	Robert Csalodi, Zsolt Bagyura, Janos Abonyi
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Clustering algorithms Gaussian mixture model survival analysis Weibull survival distribution model Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/810e779ce8d340ef824e1e7c232fc683
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id	oai:doaj.org-article:810e779ce8d340ef824e1e7c232fc683
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spelling	oai:doaj.org-article:810e779ce8d340ef824e1e7c232fc6832021-11-20T00:00:55ZMixture of Survival Analysis Models-Cluster-Weighted Weibull Distributions2169-353610.1109/ACCESS.2021.3127576https://doaj.org/article/810e779ce8d340ef824e1e7c232fc6832021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9612212/https://doaj.org/toc/2169-3536Survival analysis is a widely used method to establish a connection between a time to event outcome and a set of variables. The goal of this work is to improve the accuracy of the widely applied parametric survival models. This work highlights that accurate and interpretable survival analysis models can be identified by clustering-based exploration of the operating regions of local survival models. The key idea is that when operating regions of local Weibull distributions are represented by Gaussian mixture models, the parameters of the mixture-of-Weibull model can be identified by a clustering algorithm. The proposed method is utilised in three case studies. The examples cover studying the dropout rate of university students, calculating the remaining useful life of lithium-ion batteries, and determining the chances of survival of prostate cancer patients. The results demonstrate the wide applicability of the method and the benefits of clustering-based identification of local Weibull models.Robert CsalodiZsolt BagyuraJanos AbonyiIEEEarticleClustering algorithmsGaussian mixture modelsurvival analysisWeibull survival distribution modelElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 152288-152299 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Clustering algorithms Gaussian mixture model survival analysis Weibull survival distribution model Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Clustering algorithms Gaussian mixture model survival analysis Weibull survival distribution model Electrical engineering. Electronics. Nuclear engineering TK1-9971 Robert Csalodi Zsolt Bagyura Janos Abonyi Mixture of Survival Analysis Models-Cluster-Weighted Weibull Distributions
description	Survival analysis is a widely used method to establish a connection between a time to event outcome and a set of variables. The goal of this work is to improve the accuracy of the widely applied parametric survival models. This work highlights that accurate and interpretable survival analysis models can be identified by clustering-based exploration of the operating regions of local survival models. The key idea is that when operating regions of local Weibull distributions are represented by Gaussian mixture models, the parameters of the mixture-of-Weibull model can be identified by a clustering algorithm. The proposed method is utilised in three case studies. The examples cover studying the dropout rate of university students, calculating the remaining useful life of lithium-ion batteries, and determining the chances of survival of prostate cancer patients. The results demonstrate the wide applicability of the method and the benefits of clustering-based identification of local Weibull models.
format	article
author	Robert Csalodi Zsolt Bagyura Janos Abonyi
author_facet	Robert Csalodi Zsolt Bagyura Janos Abonyi
author_sort	Robert Csalodi
title	Mixture of Survival Analysis Models-Cluster-Weighted Weibull Distributions
title_short	Mixture of Survival Analysis Models-Cluster-Weighted Weibull Distributions
title_full	Mixture of Survival Analysis Models-Cluster-Weighted Weibull Distributions
title_fullStr	Mixture of Survival Analysis Models-Cluster-Weighted Weibull Distributions
title_full_unstemmed	Mixture of Survival Analysis Models-Cluster-Weighted Weibull Distributions
title_sort	mixture of survival analysis models-cluster-weighted weibull distributions
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/810e779ce8d340ef824e1e7c232fc683
work_keys_str_mv	AT robertcsalodi mixtureofsurvivalanalysismodelsclusterweightedweibulldistributions AT zsoltbagyura mixtureofsurvivalanalysismodelsclusterweightedweibulldistributions AT janosabonyi mixtureofsurvivalanalysismodelsclusterweightedweibulldistributions
_version_	1718419843731947520

Mixture of Survival Analysis Models-Cluster-Weighted Weibull Distributions

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