JACIII Vol.4 No.4 pp. 294-301
doi: 10.20965/jaciii.2000.p0294


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

March 23, 2000
August 7, 2000
July 20, 2000
M-of-N learning, Rule extraction, Medical diagnosis

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.

Cite this article as:
Yoichi Hayashi, Rudy Setiono, and Katsumi Yoshida, “Learning M-of-N Concepts for Medical Diagnosis Using Neural Networks,” J. Adv. Comput. Intell. Intell. Inform., Vol.4, No.4, pp. 294-301, 2000.
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Last updated on Mar. 01, 2021