TY - GEN
T1 - Quantitative assessment of estimation approaches for mining over incomplete data in complex biomedical spaces
T2 - 6th International Conference on Practical Applications of Computational Biology and Bioinformatics, PACBB'12
AU - Bisbal, Jesus
AU - Engelbrecht, Gerhard
AU - Frangi, Alejandro F.
PY - 2012
Y1 - 2012
N2 - Biomedical data sources are typically compromised by fragmented data records. This incompleteness of data reduces the confidence gained from the application of mining algorithms. In this paper an approach to approximate missing data items is presented, which enables data mining processes to be applied on a larger data set. The proposed framework is based on a case-based reasoning infrastructure which is used to identify those data entries that are more appropriate to support the approximation of missing values. Moreover, the framework is evaluated in the context of a complex biomedical domain: intracranial cerebral aneurysms. The dataset used includes a wide diversity of advanced features obtained from clinical data, morphological analysis, and hemodynamic simulations. The best feature estimations achieved errors of only 7%. There are, however, large differences between the estimation accuracy achieved with different features.
AB - Biomedical data sources are typically compromised by fragmented data records. This incompleteness of data reduces the confidence gained from the application of mining algorithms. In this paper an approach to approximate missing data items is presented, which enables data mining processes to be applied on a larger data set. The proposed framework is based on a case-based reasoning infrastructure which is used to identify those data entries that are more appropriate to support the approximation of missing values. Moreover, the framework is evaluated in the context of a complex biomedical domain: intracranial cerebral aneurysms. The dataset used includes a wide diversity of advanced features obtained from clinical data, morphological analysis, and hemodynamic simulations. The best feature estimations achieved errors of only 7%. There are, however, large differences between the estimation accuracy achieved with different features.
UR - http://www.scopus.com/inward/record.url?scp=84861208613&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-28839-5_7
DO - 10.1007/978-3-642-28839-5_7
M3 - Conference contribution
AN - SCOPUS:84861208613
SN - 9783642288388
T3 - Advances in Intelligent and Soft Computing
SP - 63
EP - 71
BT - 6th International Conference on Practical Applications of Computational Biology and Bioinformatics
Y2 - 28 March 2012 through 30 March 2012
ER -