title: A cross-validation study to select a classification procedure for clinical diagnosis based on proteomic mass spectrometry.
abstract: We present an approach to construct a classification rule based on the mass spectrometry data provided by the organizers of the "Classification Competition on Clinical Mass Spectrometry Proteomic Diagnosis Data." Before constructing a classification rule, we attempted to pre-process the data and to select features of the spectra that were likely due to true biological signals (i.e., peptides/proteins). As a result, we selected a set of 92 features. To construct the classification rule, we considered eight methods for selecting a subset of the features, combined with seven classification methods. The performance of the resulting 56 combinations was evaluated by using a cross-validation procedure with 1000 re-sampled data sets. The best result, as indicated by the lowest overall misclassification rate, was obtained by using the whole set of 92 features as the input for a support-vector machine (SVM) with a linear kernel. This method was therefore used to construct the classification rule. For the training data set, the total error rate for the classification rule, as estimated by using leave-one-out cross-validation, was equal to 0.16, with the sensitivity and specificity equal to 0.87 and 0.82, respectively.
authors.affiliation: Hasselt University, Center for Statistics. dirk.valkenborg@uhasselt.be
authors.forename: Dirk
authors.initials: D
authors.lastname: Valkenborg
authors.forename: Suzy
authors.initials: S
authors.lastname: Van Sanden
authors.forename: Dan
authors.initials: D
authors.lastname: Lin
authors.forename: Adetayo
authors.initials: A
authors.lastname: Kasim
authors.forename: Qi
authors.initials: Q
authors.lastname: Zhu
authors.forename: Philippe
authors.initials: P
authors.lastname: Haldermans
authors.forename: Ivy
authors.initials: I
authors.lastname: Jansen
authors.forename: Ziv
authors.initials: Z
authors.lastname: Shkedy
authors.forename: Tomasz
authors.initials: T
authors.lastname: Burzykowski
mesh_terms: D003933:Diagnosis; D006801:Humans; D013058:Mass Spectrometry / Q000379:methods*; D040901:Proteomics; D012680:Sensitivity and Specificity