Kelly, Jemma G. and Angelov, Plamen P. and Walsh, Michael J. and Pollock, Hubert M. and Pitt, M. A. and Martin-Hirsch, Pierre L. and Martin, Frank L. (2008) A Self-Learning Fuzzy Classifier with Feature Selection for Intelligent interrogation of mid-IR spectroscopy data from exfoliative cervical cytology using selflearning classifier eClass. International Journal of Computational Intelligence Research, 4 (4). pp. 392-401. ISSN 0973-1873Full text not available from this repository.
Abstract: The development of a predictive tool for the diagnosis of cancer is required to be both specific and sensitive, providing new information to increase understanding of the disease. We set out to determine if we could achieve this, and improve the current correct diagnosis rate of cervical cancer by combining ATR-FTIR spectroscopy with a self-learning fuzzy classifier, eClass. Cytology samples were acquired from normal, lowgrade squamous intraepithelial and high-grade squamous intraepithelial patients. Interrogation of normal and precancerous lesions was performed by ATR-FTIR spectroscopy to obtain 10 spectra from each sample. Following pre-processing (baseline correction and normalization) the data were analyzed using eClass which is characterized by being automatic, datadriven, transparent, computationally-efficient, and effectively providing high classification rates for complex non-linear and multivariate problems. An important characteristic of eClass is its ability to select features automatically based on the accumulated contribution of each of a large set of initial features (wavenumbers of the spectra). In this way, the classifier structure (a fuzzy rule base) can evolve gradually to exclude confounding factors such as inter-individual variation, and develop according to the changing requirements of a data stream i.e., identify risk biomarkers of progression towards transformation. The structure of the proposed evolving fuzzy classifier consists of three two-class classifiers connected in a cascade fashion, which provide a classification rate of 77% by using ten-fold cross-validation with unknown data.
|Journal or Publication Title:||International Journal of Computational Intelligence Research|
|Subjects:||Q Science > QA Mathematics > QA75 Electronic computers. Computer science|
Q Science > QH Natural history > QH301 Biology
|Departments:||Faculty of Science and Technology > School of Computing & Communications|
Faculty of Science and Technology > Physics
Faculty of Science and Technology > Lancaster Environment Centre
Faculty of Health and Medicine
|Deposited By:||Miss Jemma G. Kelly|
|Deposited On:||28 May 2009 13:39|
|Last Modified:||28 Jul 2016 00:00|
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