A Proposed Model of Diagnosis and Prescription in Oriental Medicine Using RBF Neural Networks
Cao Thang*, Eric W. Cooper**, Yukinobu Hoshino***,
Katsuari Kamei***, and Nguyen Hoang Phuong****
*Graduate School of Science and Engineering, Ritsumeikan University, 1-1-1 Noji-Higashi, Kusatsu, Shiga 525-8577, Japan
st Century Center of Excellence Program, Ritsumeikan University, 1-1-1 Noji-Higashi, Kusatsu, Shiga 525-8577, Japan
***College of Information Science and Engineering, Ritsumeikan University, 1-1-1 Noji-Higashi, Kusatsu, Shiga 525-8577, Japan
****Center of Health Information Technology, Ministry of Health of Vietnam, The 9 floors Building, 134 Nui Truc Lane, Ba Dinh Dist., Hanoi, Vietnam
In this paper, we present a computing model for diagnosis and prescription in oriental medicine. Inputs to the model are severities of symptoms observed on patients and outputs from the model are a diagnosis of disease states and treatment herbal prescriptions. First, having used rule inference with a Gaussian distribution, the most serious disease state in which the patient appears to be infected is determined. Next, an herbal prescription written in suitable herbs with reasonable amounts for treating the infected disease state is given by RBF neural networks. Finally, we show some experiments and their evaluations, and then describe our future works.
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