title: Possible contribution of artificial neural networks and linear discriminant analysis in recognition of patients with suspected atrophic body gastritis.
abstract: OBJECTIVE
  To investigate whether ANNs and LDA could recognize patients with ABG in a database, containing only clinical and biochemical variables, of a pool of patients with and without ABG, by selecting the most predictive variables and by reducing input data to the minimum.
  
  
  METHODS
  Data was collected from 350 consecutive outpatients (263 with ABG, 87 with non-atrophic gastritis and/or celiac disease [controls]). Structured questionnaires with 22 items (anagraphic, anamnestic, clinical, and biochemical data) were filled out for each patient. All patients underwent gastroscopy with biopsies. ANNs and LDA were applied to recognize patients with ABG. Experiment 1: random selection on 37 variables, experiment 2: optimization process on 30 variables, experiment 3: input data reduction on 8 variables, experiment 4: use of only clinical input data on 5 variables, and experiment 5: use of only serological variables.
  
  
  RESULTS
  In experiment 1, overall accuracies of ANNs and LDA were 96.6% and 94.6%, respectively, for predicting patients with ABG. In experiment 2, ANNs and LDA reached an overall accuracy of 98.8% and 96.8%, respectively. In experiment 3, overall accuracy of ANNs was 98.4%. In experiment 4, overall accuracies of ANNs and LDA were, respectively, 91.3% and 88.6%. In experiment 5, overall accuracies of ANNs and LDA were, respectively, 97.7% and 94.5%.
  
  
  CONCLUSIONS
  This preliminary study suggests that advanced statistical methods, not only ANNs, but also LDA, may contribute to better address bioptic sampling during gastroscopy in a subset of patients in whom ABG may be suspected on the basis of aspecific gastrointestinal symptoms or non-digestive disorders.
authors.affiliation: Digestive and Liver Disease Unit, II Medical School, Sant'Andrea Hospital, University La Sapienza, Rome, Italy.
authors.forename: Edith
authors.initials: E
authors.lastname: Lahner
authors.forename: Enzo
authors.initials: E
authors.lastname: Grossi
authors.forename: Marco
authors.initials: M
authors.lastname: Intraligi
authors.forename: Massimo
authors.initials: M
authors.lastname: Buscema
authors.forename: Vito-D
authors.initials: VD
authors.lastname: Corleto
authors.forename: Gianfranco
authors.initials: G
authors.lastname: Delle Fave
authors.forename: Bruno
authors.initials: B
authors.lastname: Annibale
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001706:Biopsy; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D016002:Discriminant Analysis; D005260:Female; D005757:Gastritis, Atrophic / Q000175:diagnosis* / Q000473:pathology; D005773:Gastroscopy; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D012680:Sensitivity and Specificity; D011795:Surveys and Questionnaires