Feature importance of the aortic anatomy on endovascular aneurysm repair (Evar) using boruta and bayesian mcmc

Rezzy Eko Caraka, Nyityasmono Tri Nugroho, Shao Kuo Tai, Rung Ching Chen, Toni Toharudin, Bens Pardamean

Research output: Contribution to journalArticlepeer-review

30 Citations (Scopus)

Abstract

A retrospective study of the abdominal aortic aneurysm (AAA) with EVAR treated patients. The third-party collected the data from twelve vascular centres in Indonesia during 2012-2017. Patient demographics and computed tomography data were evaluated with Osirix MD Software. During five years, we had 148 EVAR cases done using Endurant stent graft (Medtronic). In this paper, we perform Bayesian modelling and selection of feature selection by Boruta. Before performing the models, we will determine the selection of dependent variables start with the Age, Class, and Sex. It will get what is important to be dependent and independent. The difference between Bayesian and the classical method is the introduction of prior information in the form of probability distributions. In addition, to determine the parameters using the Bayesian method obtained from the probability statement. Parameter estimation in Bayesian is no longer a point estimate but, on the contrary, is a statistical distribution. In other words, Bayesian states that a parameter is a variable that has a distribution. Bayesian has become a popular method in modern statistical analysis. Bayesian is applied to a broad spectrum in the scientific and research fields. Bayesian data analysis involves learning from data that uses probability models for many observations and some information to be studied. In other words, analysing statistical models are by combining prior knowledge about the model or parameters of the model. In a nutshell, the simulation results obtained modelling with Bayesian-ZIP-MCMC R2 87.52 and Bayesian-Boruta R2 88.28%.

Original languageEnglish
Article number22
JournalCommunications in Mathematical Biology and Neuroscience
Volume2020
DOIs
Publication statusPublished - 2020

Keywords

  • Bayesian
  • Boruta
  • Feature selection
  • MCMC
  • Orta

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