M. Juhola, K. Viikki, J. Laurikkala, Y. Auramo, E. Kentala, I. Pyykkö
{"title":"Application of artificial intelligence in audiology","authors":"M. Juhola, K. Viikki, J. Laurikkala, Y. Auramo, E. Kentala, I. Pyykkö","doi":"10.1080/010503901300007209","DOIUrl":null,"url":null,"abstract":"In this paper, machine learning methods based on artificial intelligence theory are applied to the computer-aided decision making of some otoneurological diseases, for example Me´nie`re's disease. Three methods explored are decision trees, genetic algorithms and neural networks. By using such a machine learning method, the decision-making program is trained with a representative training set of cases and tested with another set. The machine learning methods are useful also for our otoneurological expert system, One, which is based on a pattern recognition approach. The methods are able to differentiate most of the cases tested between the six diseases included, provided that a sufficiently large training set is available.","PeriodicalId":76516,"journal":{"name":"Scandinavian audiology","volume":"30 1","pages":"97 - 99"},"PeriodicalIF":0.0000,"publicationDate":"2001-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/010503901300007209","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scandinavian audiology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/010503901300007209","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
In this paper, machine learning methods based on artificial intelligence theory are applied to the computer-aided decision making of some otoneurological diseases, for example Me´nie`re's disease. Three methods explored are decision trees, genetic algorithms and neural networks. By using such a machine learning method, the decision-making program is trained with a representative training set of cases and tested with another set. The machine learning methods are useful also for our otoneurological expert system, One, which is based on a pattern recognition approach. The methods are able to differentiate most of the cases tested between the six diseases included, provided that a sufficiently large training set is available.