{"title":"模糊教学输入神经专家系统及其在医学诊断中的应用","authors":"Yoichi Hayashi","doi":"10.1016/1069-0115(94)90019-1","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":100668,"journal":{"name":"Information Sciences - Applications","volume":"1 1","pages":"Pages 47-58"},"PeriodicalIF":0.0000,"publicationDate":"1994-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/1069-0115(94)90019-1","citationCount":"0","resultStr":"{\"title\":\"Neural expert system using fuzzy teaching input and its application to medical diagnosis\",\"authors\":\"Yoichi Hayashi\",\"doi\":\"10.1016/1069-0115(94)90019-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>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.</p></div>\",\"PeriodicalId\":100668,\"journal\":{\"name\":\"Information Sciences - Applications\",\"volume\":\"1 1\",\"pages\":\"Pages 47-58\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1994-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1016/1069-0115(94)90019-1\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Information Sciences - Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/1069011594900191\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Sciences - Applications","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/1069011594900191","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Neural expert system using fuzzy teaching input and its application to medical diagnosis
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.