title: Mass spectrometry cancer data classification using wavelets and genetic algorithm.
abstract: This paper introduces a hybrid feature extraction method applied to mass spectrometry (MS) data for cancer classification. Haar wavelets are employed to transform MS data into orthogonal wavelet coefficients. The most prominent discriminant wavelets are then selected by genetic algorithm (GA) to form feature sets. The combination of wavelets and GA yields highly distinct feature sets that serve as inputs to classification algorithms. Experimental results show the robustness and significant dominance of the wavelet-GA against competitive methods. The proposed method therefore can be applied to cancer classification models that are useful as real clinical decision support systems for medical practitioners.
authors.affiliation: Centre for Intelligent Systems Research (CISR), Deakin University, Waurn Ponds Campus, Victoria 3216, Australia. Electronic address: thanh.nguyen@deakin.edu.au.
authors.forename: Thanh
authors.initials: T
authors.lastname: Nguyen
authors.affiliation: Centre for Intelligent Systems Research (CISR), Deakin University, Waurn Ponds Campus, Victoria 3216, Australia.
authors.forename: Saeid
authors.initials: S
authors.lastname: Nahavandi
authors.affiliation: Centre for Intelligent Systems Research (CISR), Deakin University, Waurn Ponds Campus, Victoria 3216, Australia.
authors.forename: Douglas
authors.initials: D
authors.lastname: Creighton
authors.affiliation: Centre for Intelligent Systems Research (CISR), Deakin University, Waurn Ponds Campus, Victoria 3216, Australia.
authors.forename: Abbas
authors.initials: A
authors.lastname: Khosravi
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D005260:Female; D005823:Genetics; D006801:Humans; D008297:Male; D013058:Mass Spectrometry; D009369:Neoplasms / Q000145:classification*; D058067:Wavelet Analysis
keywords: Cancer classification; Feature extraction; Genetic algorithm; Mass spectrometry data; Wavelet transformation