Combining neural network predictions for medical diagnosis.
We present our results from combining the predictions of an ensemble of neural networks for the diagnosis of hepatobiliary disorders. To improve the accuracy of the diagnosis, we train the second level networks using the outputs of the first level networks as input data. The second level networks achieve an accuracy that is higher than that of the individual networks in the first level. Compared to the simple method which averages the outputs of the first level networks, the second level networks are also more accurate. We discuss how the overall predictive accuracy can be improved by introducing bias during the training of the level one networks.
Hayashi
Yoichi
Y
Department of Computer Science, Meiji University, 1-1-1 Higashimita, Tama-ku, 214-8571, Kawasaki, Japan. hayashiy@cs.meiji.ac.jp
Setiono
Rudy
R
D015982:Bias; D006528:Carcinoma, Hepatocellular / Q000175:diagnosis*; D002769:Cholelithiasis / Q000175:diagnosis*; D003936:Diagnosis, Computer-Assisted; D003937:Diagnosis, Differential; D006801:Humans; D008103:Liver Cirrhosis / Q000175:diagnosis*; D008108:Liver Diseases, Alcoholic / Q000175:diagnosis*; D008111:Liver Function Tests; D008113:Liver Neoplasms / Q000175:diagnosis*; D016571:Neural Networks, Computer