Learning M-of-N Concepts for Medical Diagnosis Using Neural Networks
Yoichi Hayashi*, Rudy Setiono** and Katsumi Yoshida***
*Department of Computer Science, Meiji University, 1-1-1 Higashimita, Tama-ku, Kawasaki 214-8571, Japan
**School of Computing, National University of Singapore Lower Kent Ridge Road, Singapore 119260
***Department of Preventive Medicine, St. Marianna University School of Medicine, 2-16-1 Sugao, Miyamae-ku, Kawasaki 216-8511, Japan
Records in a medical dataset may be best characterized by M-of-N concepts. For example, a patient showing at least 2 of the 4 symptoms is likely to be diagnosed as having a certain illness. In this paper, we describe how feedforward neural networks can be used to learn such concepts. We train a network where each input in the data can only have one of two possible values, -1 or 1 and apply the hyperbolic tangent function to each connection from the input layer to the hidden layer of the network before the hidden unit activations are computed. By applying this squashing function, the activation values at the hidden units are effectively computed as the hyperbolic tangent (or the sigmoid) of the weighted inputs, where the weights have magnitudes that are near one. By restricting the inputs and the weights to binary ’ values either -1 or 1, the extraction of the M-of-N concepts from networks becomes trivial. We show how this approach can be used to learn concise and accurate the M-of-N concepts for the diagnosis of hepatobiliary disorders.
This article is published under a Creative Commons Attribution-NoDerivatives 4.0 International License.