Acute appendicitis diagnosis using artificial neural networks

作者：Park Sung Yun; Kim Sung Min^*

来源：Technology and Health Care, 2015, 23(s2): S559-S565.

DOI：10.3233/THC-150994

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

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.

• 出版日期2015