Bagged super wavelets reduction for boosted prostate cancer classification of seldi-tof mass spectral serum profiles
Donald, David, Hancock, Tim, Coomans, Danny, and Everingham, Yvette (2006) Bagged super wavelets reduction for boosted prostate cancer classification of seldi-tof mass spectral serum profiles. Chemometrics and Intelligent Laboratory Systems, 82 (1-2). pp. 2-7.
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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.
|Item Type:||Article (Refereed Research - C1)|
|Keywords:||wavelets; prostate cancer; mass spectra|
|Date Deposited:||15 Jun 2009 01:20|
|SEO Codes:||92 HEALTH > 9299 Other Health > 929999 Health not elsewhere classified @ 100%|
|Citation Count from Web of Science||