{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T00:12:16Z","timestamp":1777421536874,"version":"3.51.4"},"reference-count":19,"publisher":"SAGE Publications","issue":"10","license":[{"start":{"date-parts":[[1999,10,1]],"date-time":"1999-10-01T00:00:00Z","timestamp":938736000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Ann Otol Rhinol Laryngol"],"published-print":{"date-parts":[[1999,10]]},"abstract":"<jats:p>Data on patients with Meniere's disease, vestibular schwannoma, traumatic vertigo, sudden deafness, benign paroxysmal positional vertigo, or vestibular neuritis were retrieved from the database of otoneurologic expert system ONE for the development and testing of a genetic algorithm (GA). The accuracy of the diagnostic rules in solving the test cases was 81%, 91%, 92%, 95%, 96%, and 98% for the respective diseases. The best rules retrieved from the GA were described by a set of questions with the most likely answers. The most important questions concerned the duration of hearing loss and the occurrence of head injury. The validity and structure of the rules created with a GA can be analyzed in detail. For rare diseases, some other reasoning process can be used, for example, case-based reasoning.<\/jats:p>","DOI":"10.1177\/000348949910801005","type":"journal-article","created":{"date-parts":[[2014,10,3]],"date-time":"2014-10-03T14:38:05Z","timestamp":1412347085000},"page":"948-954","source":"Crossref","is-referenced-by-count":18,"title":["Discovering Diagnostic Rules from a Neurotologic Database with Genetic Algorithms"],"prefix":"10.1177","volume":"108","author":[{"given":"Erna","family":"Kentala","sequence":"first","affiliation":[{"name":"Helsinki, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ilmari","family":"Pyykk\u00f6","sequence":"additional","affiliation":[{"name":"Stockholm, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jorma","family":"Laurikkala","sequence":"additional","affiliation":[{"name":"Tampere, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Martti","family":"Juhola","sequence":"additional","affiliation":[{"name":"Tampere, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[1999,10,1]]},"reference":[{"key":"bibr1-000348949910801005","volume-title":"Adaptation in natural and artificial systems","author":"Holland JH","year":"1975"},{"key":"bibr2-000348949910801005","volume-title":"Genetic algorithms in search, optimization, and machine learning","author":"Goldberg DE","year":"1989"},{"key":"bibr3-000348949910801005","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/3927.001.0001"},{"key":"bibr4-000348949910801005","first-page":"113","volume":"37","author":"Goldberg DE","year":"1994","journal-title":"Commun Assoc Comput Machinery"},{"key":"bibr5-000348949910801005","doi-asserted-by":"publisher","DOI":"10.1126\/science.8346439"},{"key":"bibr6-000348949910801005","volume-title":"A comparison of genetic algorithms and other machine learning systems on a complex classification task from common disease research [Dissertation]","author":"Gongdon CB","year":"1995"},{"key":"bibr7-000348949910801005","unstructured":"Laurikkala J, Juhola M. Learning diagnostic rules from a urological database using genetic algorithms. 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