title: Diagnostics for Statistical Variable Selection Methods for Prediction of Peptic Ulcer Disease inHelicobacter pyloriInfection.
abstract: BACKGROUND
  The development of accurate classification models depends upon the methods used to identify the most relevant variables. The aim of this article is to evaluate variable selection methods to identify important variables in predicting a binary response using nonlinear statistical models. Our goals in model selection include producing non-overfitting stable models that are interpretable, that generate accurate predictions and have minimum bias. This work was motivated by data on clinical and laboratory features ofHelicobacter pyloriinfections obtained from 60 individuals enrolled in a prospective observational study.
  
  
  RESULTS
  We carried out a comprehensive performance comparison of several nonlinear classification models over theH. pyloridata set. We compared variable selection results by Multivariate Adaptive Regression Splines (MARS), Logistic Regression with regularization, Generalized Additive Models (GAMs) and Bayesian Variable Selection in GAMs. We found that the MARS model approach has the highest predictive power because the nonlinearity assumptions of candidate predictors are strongly satisfied, a finding demonstrated via deviance chi-square testing procedures in GAMs.
  
  
  CONCLUSIONS
  Our results suggest that the physiological free amino acids citrulline, histidine, lysine and arginine are the major features for predictingH. pyloripeptic ulcer disease on the basis of amino acid profiling.
authors.affiliation: Departments of Preventive Medicine and Community Health, University of Texas Medical Branch (UTMB), Galveston, TX, USA ; Institute for Translational Sciences, University of Texas Medical Branch, Galveston, TX, USA.
authors.forename: Hyunsu
authors.initials: H
authors.lastname: Ju
authors.affiliation: Sealy Center for Molecular Medicine, University of Texas Medical Branch, Galveston, TX, USA ; Institute for Translational Sciences, University of Texas Medical Branch, Galveston, TX, USA.
authors.forename: Allan R
authors.initials: AR
authors.lastname: Brasier
authors.affiliation: Departments of Medicine, Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX 77030, USA.
authors.forename: Alexander
authors.initials: A
authors.lastname: Kurosky
authors.affiliation: Departments of Biochemistry and Molecular Biology, University of Texas Medical Branch, Galveston, TX, USA.
authors.forename: Bo
authors.initials: B
authors.lastname: Xu
authors.affiliation: Department of Pediatrics, University of Texas Medical Branch, Galveston, TX, USA ; Department of Microbiology & Immunology, University of Texas Medical Branch, Galveston, TX, USA.
authors.forename: Victor E
authors.initials: VE
authors.lastname: Reyes
authors.affiliation: Departments of Medicine, Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX 77030, USA ; Baylor College of Medicine, Houston TX, USA.
authors.forename: David Y
authors.initials: DY
authors.lastname: Graham
keywords: Amino acid analysis; Classification; Helicobacter pylori; Peptic ulcer disease; Variable selection