title: Acute appendicitis diagnosis using artificial neural networks.
abstract: BACKGROUND
  Artificial neural networks is one of pattern analyzer method which are rapidly applied on a bio-medical field.
  
  
  OBJECTIVE
  The aim of this research was to propose an appendicitis diagnosis system using artificial neural networks (ANNs).
  
  
  METHODS
  Data from 801 patients of the university hospital in Dongguk were used to construct artificial neural networks for diagnosing appendicitis and acute appendicitis. A radial basis function neural network structure (RBF), a multilayer neural network structure (MLNN), and a probabilistic neural network structure (PNN) were used for artificial neural network models. The Alvarado clinical scoring system was used for comparison with the ANNs.
  
  
  RESULTS
  The accuracy of the RBF, PNN, MLNN, and Alvarado was 99.80%, 99.41%, 97.84%, and 72.19%, respectively. The area under ROC (receiver operating characteristic) curve of RBF, PNN, MLNN, and Alvarado was 0.998, 0.993, 0.985, and 0.633, respectively.
  
  
  CONCLUSIONS
  The proposed models using ANNs for diagnosing appendicitis showed good performances, and were significantly better than the Alvarado clinical scoring system (p < 0.001). With cooperation among facilities, the accuracy for diagnosing this serious health condition can be improved.
authors.forename: Sung Yun
authors.initials: SY
authors.lastname: Park
authors.forename: Sung Min
authors.initials: SM
authors.lastname: Kim
mesh_terms: D000208:Acute Disease; D000293:Adolescent; D000328:Adult; D001064:Appendicitis / Q000175:diagnosis*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D006785:Hospitals, University; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D012372:ROC Curve; D056910:Republic of Korea; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index; D055815:Young Adult
keywords: Alvarado clinical scoring system; acute appendicitis; artificial neural network; clinical scoring system