Neural expert system using fuzzy teaching input and its application to medical diagnosis

Yoichi Hayashi
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Abstract

This paper first proposes a fuzzy neural network and the learning method using fuzzy teaching input. As an application, a fuzzy neural expert system (FNES) for diagnosing hepatobiliary disorders has been developed. We used a real medical database containing the results of nine biochemical tests of four hepatobiliary disorders. After learning by using training data (373 patients), the proposed system correctly diagnosed 77.3% of test (external) data from 163 previously unseen patients and correctly diagnosed 100% of the training data. Conversely, the diagnostic accuracy of the linear discriminant analysis was 63.2% of the test data and 67.0% of the training data.

模糊教学输入神经专家系统及其在医学诊断中的应用
本文首先提出了模糊神经网络和模糊教学输入的学习方法。作为一种应用,开发了一种用于肝胆疾病诊断的模糊神经专家系统(FNES)。我们使用了一个真实的医学数据库,其中包含四种肝胆疾病的九项生化测试结果。通过使用训练数据(373名患者)进行学习后,该系统正确诊断了163名以前未见过的患者的77.3%的测试(外部)数据,并正确诊断了100%的训练数据。相反,线性判别分析的诊断准确率为测试数据的63.2%,训练数据的67.0%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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