Bagged super wavelets reduction for boosted prostate cancer classification of seldi-tof mass spectral serum profiles

Journal Publication ResearchOnline@JCU
Donald, David;Hancock, Tim;Coomans, Danny;Everingham, Yvette
Abstract

Wavelet based analysis for mass spectrometry (MS) profiles of three groups of patients are analyzed for the purpose of developing a classification model. The first step in our model uses a DWT for feature extraction, using a linear combination of Symlets, Daubechies and Coiflets wavelet bases – collectively known as a super wavelet. Random Forests and Treeboost are then used to analyze the super wavelet coefficients to form the classification model. The method is illustrated using the publicly available prostate SELDI-TOF MS data from the American National Cancer Institute (NCI). The NCI data consists of 322 MS profiles with 15154 M / Z ratios, comprising of 69 malignant, 190 benign and 63 control patients, which we randomly divided into 70% training and 30% testing. From the Random Forest models, the super wavelet performed 2.7% to 5.7% better than other single wavelet types to give a 100% test set prediction rate for cancerous patients.

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Volume

82

ISBN/ISSN

1873-3239

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Issue

1-2

Pages Count

6

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Publisher

Elsevier

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Publisher Location

Leiden, Netherlands

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EISSN

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DOI

10.1016/j.chemolab.2005.08.007