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- Title
Acute appendicitis diagnosis using artificial neural networks.
- Authors
Sung Yun Park; Sung Min Kim; Park, Sung Yun; Kim, Sung Min
- Abstract
<bold>Background: </bold>Artificial neural networks is one of pattern analyzer method which are rapidly applied on a bio-medical field.<bold>Objective: </bold>The aim of this research was to propose an appendicitis diagnosis system using artificial neural networks (ANNs).<bold>Methods: </bold>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.<bold>Results: </bold>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.<bold>Conclusions: </bold>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.
- Subjects
SOUTH Korea; APPENDICITIS diagnosis; ARTIFICIAL neural networks; RADIAL basis functions; APPENDIX diseases; MEDICAL research; MEDICAL publishing; ACADEMIC medical centers; SEVERITY of illness index; RECEIVER operating characteristic curves; ACUTE diseases; COMPUTER-aided diagnosis
- Publication
Technology & Health Care, 2015, Vol 23, pS559
- ISSN
0928-7329
- Publication type
journal article
- DOI
10.3233/THC-150994