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Proceedings Paper

Optimizing the feature set for a Bayesian network for breast cancer diagnosis using genetic algorithm techniques
Author(s): Xiao Hui Wang; Bin Zheng; Yuan-Hsiang Chang; Walter F. Good
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Paper Abstract

This study investigates the degree to which the performance of Bayesian belief networks (BBNs), for computer-assisted diagnosis of breast cancer, can be improved by optimizing their input feature sets using a genetic algorithm (GA). 421 cases (all women) were used in this study, of which 92 were positive for breast cancer. Each case contained both non-image information and image information derived from mammograms by radiologists. A GA was used to select an optimal subset of features, from a total of 21, to use as the basis for a BBN classifier. The figure-of-merit used in the GA's evaluation of feature subsets was Az, the area under the ROC curve produced by the corresponding BBN classifier. For each feature subset evaluated by the GA, a BBN was developed to classify positive and negative cases. Overall performance of the BBNs was evaluated using a jackknife testing method to calculate Az, for their respective ROC curves. The Az value of the BBN incorporating all 21 features was 0.851 plus or minus 0.012. After a 93 generation search, the GA found an optimal feature set with four non-image and four mammographic features, which achieved an Az value of 0.927 plus or minus 0.009. This study suggests that GAs are a viable means to optimize feature sets, and optimizing feature sets can result in significant performance improvements.

Paper Details

Date Published: 21 May 1999
PDF: 7 pages
Proc. SPIE 3661, Medical Imaging 1999: Image Processing, (21 May 1999); doi: 10.1117/12.348560
Show Author Affiliations
Xiao Hui Wang, Univ. of Pittsburgh (United States)
Bin Zheng, Univ. of Pittsburgh (United States)
Yuan-Hsiang Chang, Univ. of Pittsburgh (United States)
Walter F. Good, Univ. of Pittsburgh (United States)

Published in SPIE Proceedings Vol. 3661:
Medical Imaging 1999: Image Processing
Kenneth M. Hanson, Editor(s)

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