TY - GEN
T1 - Variable optimisation of medical image data by the learning Bayesian network reasoning
AU - Orun, A. B.
AU - Aydin, N.
PY - 2010
Y1 - 2010
N2 - The method proposed here uses Bayesian non-linear classifier to select optimal subset of attributes to avoid redundant variables and reduce data uncertainty in the classification process often used in medical diagnosis. The method also exploits the structural reasoning ability of Bayesian Networks (BN) to optimize large number of attributes to prevent overfitting, meanwhile it maintains the high classification accuracy. This process simplifies the complex data analyses and may lead to a cost reduction in clinical data acquisition process.
AB - The method proposed here uses Bayesian non-linear classifier to select optimal subset of attributes to avoid redundant variables and reduce data uncertainty in the classification process often used in medical diagnosis. The method also exploits the structural reasoning ability of Bayesian Networks (BN) to optimize large number of attributes to prevent overfitting, meanwhile it maintains the high classification accuracy. This process simplifies the complex data analyses and may lead to a cost reduction in clinical data acquisition process.
UR - http://www.scopus.com/inward/record.url?scp=78650814424&partnerID=8YFLogxK
U2 - 10.1109/IEMBS.2010.5626046
DO - 10.1109/IEMBS.2010.5626046
M3 - Conference contribution
C2 - 21095794
AN - SCOPUS:78650814424
SN - 9781424441235
T3 - 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10
SP - 4554
EP - 4557
BT - 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10
T2 - 2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10
Y2 - 31 August 2010 through 4 September 2010
ER -