title: Contribution of artificial neural networks to the classification and treatment of patients with uninvestigated dyspepsia.
abstract: OBJECTIVE
  To verify whether symptoms reported by patients with uninvestigated dyspepsia might be helpful in either classifying functional from organic dyspepsia (1st experiment), or recognising which Helicobacter pylori infected patients may benefit from eradication therapy (2nd experiment).
  
  
  METHODS
  We compared the performance of artificial neural networks and linear discriminant analysis in two experiments on a database including socio-demographic features, past medical history, alarming symptoms, and symptoms at presentation of 860 patients with uninvestigated dyspepsia enrolled in a large observational multi-centre Italian study.
  
  
  RESULTS
  In the 1st experiment, the best prediction for organic disease was given by the Sine Net model (specificity of 87.6% with 13 patients misclassified) and the best prediction for functional dyspepsia by the FF Bp model (sensitivity of 83.4% with 56 patients misclassified). The highest global accuracy of linear discriminant analysis was 65.1%, with 150 patients misclassified. In the 2nd experiment, the highest predictive performance was provided by the SelfDASn model: all infected patients who became symptom-free after successful eradicating treatment were correctly classified, whereas nine errors were made in forecasting patients who did not benefit from such a therapy. The highest global performance of linear discriminant analysis was 53.2%, with 37 patients misclassified.
  
  
  CONCLUSIONS
  In patients with uninvestigated dyspepsia, artificial neural networks might have potential for categorising those affected by either organic or functional dyspepsia, as well as for identifying all Helicobacter pylori infected dyspeptic patients who will benefit from eradication.
authors.affiliation: Division of Gastroenterology, Casa Sollievo della Sofferenza Hospital, IRCCS, I-71013 San Giovanni Rotondo, Italy. angelo.andriulli@tin.it
authors.forename: A
authors.initials: A
authors.lastname: Andriulli
authors.forename: E
authors.initials: E
authors.lastname: Grossi
authors.forename: M
authors.initials: M
authors.lastname: Buscema
authors.forename: V
authors.initials: V
authors.lastname: Festa
authors.forename: N M
authors.initials: NM
authors.lastname: Intraligi
authors.forename: P
authors.initials: P
authors.lastname: Dominici
authors.forename: R
authors.initials: R
authors.lastname: Cerutti
authors.forename: F
authors.initials: F
authors.lastname: Perri
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001185:Artificial Intelligence; D019992:Databases as Topic; D016002:Discriminant Analysis; D004415:Dyspepsia / Q000145:classification* / Q000175:diagnosis / Q000628:therapy*; D005260:Female; D016481:Helicobacter Infections / Q000175:diagnosis / Q000628:therapy*; D016480:Helicobacter pylori / Q000302:isolation & purification; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D011446:Prospective Studies; D012680:Sensitivity and Specificity; D011795:Surveys and Questionnaires