DocumentCode :
2358580
Title :
A comparative study of feature selection methods for probabilistic neural networks in cancer classification
Author :
Huang, Chenn-Jung ; Liao, Wei-Chen
Author_Institution :
Inst. of Learning Technol., Nat. Hualien Teachers Coll., Hualie, Taiwan
fYear :
2003
fDate :
3-5 Nov. 2003
Firstpage :
451
Lastpage :
458
Abstract :
Accurate diagnosis and classification is the key issue for the optimal treatment of cancer patients. Several studies demonstrate that cancer classification can be estimated with high accuracy, sensitivity and specificity from microarray-based gene expression profiling using artificial neural networks. In this paper, a comprehensive study was undertaken to investigate the capability of the probabilistic neural networks (PNN) associated with a feature selection method, a so-called signal-to-noise statistic, in the application of cancer classification. The signal-to-noise statistic, which represents the correlation with the class distinction, is used to select the marker genes and trim the dimension of data samples for the PNN. The experimental results show that the association of the probabilistic neural network with the signal-to-noise statistic can achieve superior classification results for two types of acute leukemias and five categories of embryonal tumors of central nervous system with satisfactory computation speed. Furthermore, the signal-to-noise statistic analysis provides candidate genes for future study in understanding the disease process and the identification of potential targets for therapeutic intervention.
Keywords :
cancer; feature extraction; medical computing; neural nets; statistical analysis; tumours; accurate classification; accurate diagnosis; acute leukemia; artificial neural network; cancer classification; cancer patient; cancer treatment; candidate genes; central nervous system; computation speed; disease process; embryonal tumor; feature selection; marker genes; microarray-based gene expression profiling; optimal treatment; probabilistic neural network; signal-to-noise statistic; therapeutic intervention; Artificial neural networks; Biological neural networks; Cancer; Embryo; Gene expression; Medical treatment; Neoplasms; Neural networks; Sensitivity and specificity; Statistics;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Tools with Artificial Intelligence, 2003. Proceedings. 15th IEEE International Conference on
ISSN :
1082-3409
Print_ISBN :
0-7695-2038-3
Type :
conf
DOI :
10.1109/TAI.2003.1250224
Filename :
1250224
Link To Document :
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