Acute appendicitis diagnosis using artificial neural networks

Sung Yun Park, Sung Min Kim

Research output: Contribution to journalArticlepeer-review

30 Scopus citations

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.

Original languageEnglish
Pages (from-to)S559-S565
JournalTechnology and Health Care
Volume23
DOIs
StatePublished - 17 Jun 2015

Keywords

  • acute appendicitis
  • Alvarado clinical scoring system
  • artificial neural network
  • clinical scoring system

Fingerprint

Dive into the research topics of 'Acute appendicitis diagnosis using artificial neural networks'. Together they form a unique fingerprint.

Cite this