Handling high level of censoring for endovascular aortic repair risk prediction

Omneya Attallah, Xianghong Ma

Research output: Chapter in Book/Published conference outputConference publication


Feature selection is important in medical field for many reasons. However, selecting important variables is a difficult task with the presence of censoring that is a unique feature in survival data analysis. This paper proposed an approach to deal with the censoring problem in endovascular aortic repair survival data through Bayesian networks. It was merged and embedded with a hybrid feature selection process that combines cox's univariate analysis with machine learning approaches such as ensemble artificial neural networks to select the most relevant predictive variables. The proposed algorithm was compared with common survival variable selection approaches such as; least absolute shrinkage and selection operator LASSO, and Akaike information criterion AIC methods. The results showed that it was capable of dealing with high censoring in the datasets. Moreover, ensemble classifiers increased the area under the roc curves of the two datasets collected from two centers located in United Kingdom separately. Furthermore, ensembles constructed with center 1 enhanced the concordance index of center 2 prediction compared to the model built with a single network. Although the size of the final reduced model using the neural networks and its ensembles is greater than other methods, the model outperformed the others in both concordance index and sensitivity for center 2 prediction. This indicates the reduced model is more powerful for cross center prediction.

Original languageEnglish
Title of host publication2015 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015
Number of pages5
ISBN (Print)978-1-4799-7591-4
Publication statusPublished - 23 Feb 2016
EventIEEE Global Conference on Signal and Information Processing - Orlando, FL, United States
Duration: 13 Dec 201516 Dec 2015


ConferenceIEEE Global Conference on Signal and Information Processing
Abbreviated titleGlobalSIP 2015
Country/TerritoryUnited States
CityOrlando, FL

Bibliographical note



  • artifical neural networks introduction
  • censoring
  • endovascular aortic repair
  • survival analysis


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