Prediction of cerebral aneurysm rupture using hemodynamic, morphologic and clinical features: A data mining approach

Jesus Bisbal, Gerhard Engelbrecht, Mari Cruz Villa-Uriol, Alejandro F. Frangi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Cerebral aneurysms pose a major clinical threat and the current practice upon diagnosis is a complex, lengthy, and costly, multi-criteria analysis, which to date is not fully understood. This paper reports the development of several classifiers predicting whether a given clinical case is likely to rupture taking into account available information of the patient and characteristics of the aneurysm. The dataset used included 157 cases, with 294 features each. The broad range of features include basic demographics and clinical information, morphological characteristics computed from the patient's medical images, as well as results gained from personalised blood flow simulations. In this premiere attempt the wealth of aneurysm-related information gained from multiple heterogeneous sources and complex simulation processes is used to systematically apply different data-mining algorithms and assess their predictive accuracy in this domain. The promising results show up to 95% classification accuracy. Moreover, the analysis also enables to confirm or reject risk factors commonly accepted or suspected in the domain.

Original languageEnglish
Title of host publicationDatabase and Expert Systems Applications - 22nd International Conference, DEXA 2011, Proceedings
Pages59-73
Number of pages15
EditionPART 2
DOIs
Publication statusPublished - 2011
Event22nd International Conference on Database and Expert Systems Applications, DEXA 2011 - Toulouse, France
Duration: 29 Aug 20112 Sept 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume6861 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference22nd International Conference on Database and Expert Systems Applications, DEXA 2011
Country/TerritoryFrance
CityToulouse
Period29/08/112/09/11

Keywords

  • aneurysm rupture
  • association rules
  • biomedicine
  • classifiers
  • complex data
  • Data mining
  • decision support
  • feature discretization
  • feature selection

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