doi: 10.7763/IJCTE.2009.V1.56
Diagnosing Appendicitis Using Backpropagation Neural Network and Bayesian Based Classifier
- 1Department of Computer Science & Engineering, National Institute of Technology, Tiruchirappalli-15
- 2Department of Computer Science & Engineering, Manonmaniam Sundaranar University Tirunelveli
Abstract
The purpose of this study was to assess the role of a Bayesian classifier and back propagation neural network classifier in the diagnosis of severity of appendicitis in patients presenting with right iliac fossa (RIF) pain using Alvarado scoring method. The input parameters of the classifier are the pain site, pain nature, nausea, previous surgery, RIF Tenderness, Rebound Tenderness, Guarding, Rigidity, Temperature, White blood cell count , Neutrophilcount and the output parameters are different classes of appendicitis namely mild (Inflammation only), moderate (Inflammation, Faceolith and Turgid) and severe (Gangrenous and Perforated) appendicitis. The methodology used was a back propagation neural network and Bayesian classifier for diagnosing Appendicitis. The data set is based on the statistics already collected about the presence of appendicitis from patients data set of around 2230 records collected from BHEL Hospital, Tiruchirappalli, India. The conclusion is that Bayesian classifier and back propagation neural network classifier can be used as an effective tool for accurately diagnosing the severity of appendicitis.
Keywords
- Data mining
- Bayesian classification
- Backpropagation Neural Networks
- Appendicitis
How to Cite
E. Sivasankar, R. S. Rajesh, and S. R. Venkateswaran, "Diagnosing Appendicitis Using Backpropagation Neural Network and Bayesian Based Classifier," International Journal of Computer Theory and Engineering, vol. 1, no. 4, pp. 358-363, 2009. https://doi.org/10.7763/IJCTE.2009.V1.56
Copyright & License
Copyright © 2009 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).