论文信息 - Hepatitis Disease Diagnosis Using Backpropagation and the Naive Bayes Classifiers

Hepatitis Disease Diagnosis Using Backpropagation and the Naive Bayes Classifiers

This study presents a comparison between Backpropagation and Naive Bayes Classifiers to diagnose hepatitis disease. Hepatitis is the general term for inflammation of the liver. The most common causes of hepatitis are the hepatotropic viruses (such as hepatitis A, B, and C) and alcohol abuse. In practice, both of these methods often compete well with more sophisticated classifiers. The performances of proposed methods are selected for each of classification tasks of hepatitis diseases. The over-all accuracy of diagnosis systems were 98% and 97% respectively.

Bekir Karlik

[1] Pedro M. Domingos,et al. On the Optimality of the Simple Bayesian Classifier under Zero-One Loss , 1997, Machine Learning.

[2] Zhi-Hua Zhou,et al. Medical diagnosis with C4.5 rule preceded by artificial neural network ensemble , 2003, IEEE Transactions on Information Technology in Biomedicine.

[3] H. R. Öz,et al. Application of Artificial Neural Networks Method in Mucus Clearance in Pulmonary Airways , 2009 .

[4] Sotiris B. Kotsiantis,et al. Increasing the Classification Accuracy of Simple Bayesian Classifier , 2004, AIMSA.

[5] Karoly Menyhardt,et al. Statistical Analyses and Artificial Neural Networks for Prognoses in Hepatitis C , 2006 .

[6] R. Cozzolongo,et al. Artificial neural networks for the prediction of response to interferon plus ribavirin treatment in patients with chronic hepatitis C. , 2004, Current pharmaceutical design.