TY - JOUR
T1 - Acute appendicitis diagnosis using artificial neural networks
AU - Park, Sung Yun
AU - Kim, Sung Min
N1 - Publisher Copyright:
© 2015 - IOS Press and the authors. All rights reserved.
PY - 2015/6/17
Y1 - 2015/6/17
N2 - 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.
AB - 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.
KW - acute appendicitis
KW - Alvarado clinical scoring system
KW - artificial neural network
KW - clinical scoring system
UR - http://www.scopus.com/inward/record.url?scp=84937711651&partnerID=8YFLogxK
U2 - 10.3233/THC-150994
DO - 10.3233/THC-150994
M3 - Article
C2 - 26410524
AN - SCOPUS:84937711651
SN - 0928-7329
VL - 23
SP - S559-S565
JO - Technology and Health Care
JF - Technology and Health Care
ER -