TY - GEN
T1 - Prediction of cerebral aneurysm rupture using hemodynamic, morphologic and clinical features
T2 - 22nd International Conference on Database and Expert Systems Applications, DEXA 2011
AU - Bisbal, Jesus
AU - Engelbrecht, Gerhard
AU - Villa-Uriol, Mari Cruz
AU - Frangi, Alejandro F.
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
KW - aneurysm rupture
KW - association rules
KW - biomedicine
KW - classifiers
KW - complex data
KW - Data mining
KW - decision support
KW - feature discretization
KW - feature selection
UR - http://www.scopus.com/inward/record.url?scp=80052797146&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-23091-2_6
DO - 10.1007/978-3-642-23091-2_6
M3 - Conference contribution
AN - SCOPUS:80052797146
SN - 9783642230905
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 59
EP - 73
BT - Database and Expert Systems Applications - 22nd International Conference, DEXA 2011, Proceedings
Y2 - 29 August 2011 through 2 September 2011
ER -