title: Continuous wavelet transform-based feature selection applied to near-infrared spectral diagnosis of cancer.
abstract: Spectrum is inherently local in nature since it can be thought of as a signal being composed of various frequency components. Wavelet transform (WT) is a powerful tool that partitions a signal into components with different frequency. The property of multi-resolution enables WT a very effective and natural tool for analyzing spectrum-like signal. In this study, a continuous wavelet transform (CWT)-based variable selection procedure was proposed to search for a set of informative wavelet coefficients for constructing a near-infrared (NIR) spectral diagnosis model of cancer. The CWT provided a fine multi-resolution feature space for selecting best predictors. A measure of discriminating power (DP) was defined to evaluate the coefficients. Partial least squares-discriminant analysis (PLS-DA) was used as the classification algorithm. A NIR spectral dataset associated to cancer diagnosis was used for experiment. The optimal results obtained correspond to the wavelet of db2. It revealed that on condition of having better performance on the training set, the optimal PLS-DA model using only 40 wavelet coefficients in 10 scales achieved the same performance as the one using all the variables in the original space on the test set: an overall accuracy of 93.8%, sensitivity of 92.5% and specificity of 96.3%. It confirms that the CWT-based feature selection coupled with PLS-DA is feasible and effective for constructing models of diagnostic cancer by NIR spectroscopy.
authors.affiliation: Key Lab of Process Analysis and Control of Sichuan Universities, Yibin University, Yibin, Sichuan 644000, China; Hospital, Yibin University, Yibin, Sichuan 644000, China.
authors.forename: Hui
authors.initials: H
authors.lastname: Chen
authors.affiliation: Key Lab of Process Analysis and Control of Sichuan Universities, Yibin University, Yibin, Sichuan 644000, China; The First Affiliated Hospital, Chongqing Medical University, Chongqing 400016, China.
authors.forename: Zan
authors.initials: Z
authors.lastname: Lin
authors.affiliation: The Affiliated Hospital, North Sichuan Medical College, Nanchong, Sichuan 637000, China.
authors.forename: Lin
authors.initials: L
authors.lastname: Mo
authors.affiliation: The First People's Hospital of Yibin, Yibin, Sichuan 644000, China.
authors.forename: Hegang
authors.initials: H
authors.lastname: Wu
authors.affiliation: Key Lab of Process Analysis and Control of Sichuan Universities, Yibin University, Yibin, Sichuan 644000, China.
authors.forename: Tong
authors.initials: T
authors.lastname: Wu
authors.affiliation: Key Lab of Process Analysis and Control of Sichuan Universities, Yibin University, Yibin, Sichuan 644000, China. Electronic address: chaotan1112@163.com.
authors.forename: Chao
authors.initials: C
authors.lastname: Tan
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D015179:Colorectal Neoplasms / Q000175:diagnosis*; D016002:Discriminant Analysis; D005260:Female; D006801:Humans; D016018:Least-Squares Analysis; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D019265:Spectroscopy, Near-Infrared / Q000379:methods*; D058067:Wavelet Analysis
keywords: Cancer; Classification; Continuous wavelets transform; Diagnosis; Spectroscopy