Literature DB >> 11318434

A novel machine learning program applied to discover otological diagnoses.

J P Laurikkala1, E L Kentala, M Juhola, I V Pyvkkö.   

Abstract

A novel machine learning system, Galactica, has been developed for knowledge discovery from databases. This system was applied to discover diagnostic rules from a patient database containing 564 cases with vestibular schwannoma, bening paroxysmal positional vertigo, Ménière's disease, sudden deafness, traumatic vertigo and vestibular neuritis diagnoses. The rules were evaluated using an independent testing set. The accuracy of rules for these diagnoses were 91%, 96%, 81%, 95%, 92% and 98%, respectively. Besides being accurate, the rules contained the five most important diagnostic questions identified in the earlier research. The knowledge presented with rules can be easily comprehended and verified.

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Year:  2001        PMID: 11318434     DOI: 10.1080/010503901300007218

Source DB:  PubMed          Journal:  Scand Audiol Suppl        ISSN: 0107-8593


  3 in total

1.  Predictive Capability of an iPad-Based Medical Device (medx) for the Diagnosis of Vertigo and Dizziness.

Authors:  Katharina Feil; Regina Feuerecker; Nicolina Goldschagg; Ralf Strobl; Thomas Brandt; Albrecht von Müller; Eva Grill; Michael Strupp
Journal:  Front Neurol       Date:  2018-02-27       Impact factor: 4.003

2.  Diagnostic accuracy and usability of the EMBalance decision support system for vestibular disorders in primary care: proof of concept randomised controlled study results.

Authors:  Doris-Eva Bamiou; Dimitris Kikidis; Thanos Bibas; Nehzat Koohi; Nora Macdonald; Christoph Maurer; Floris L Wuyts; Berina Ihtijarevic; Laura Celis; Viviana Mucci; Leen Maes; Vincent Van Rompaey; Paul Van de Heyning; Irwin Nazareth; Themis P Exarchos; Dimitrios Fotiadis; Dimitrios Koutsouris; Linda M Luxon
Journal:  J Neurol       Date:  2021-10-20       Impact factor: 6.682

3.  A Questionnaire-Based Ensemble Learning Model to Predict the Diagnosis of Vertigo: Model Development and Validation Study.

Authors:  Fangzhou Yu; Peixia Wu; Haowen Deng; Cheng Zhang; Huawei Li; Jingfang Wu; Shan Sun; Huiqian Yu; Jianming Yang; Xianyang Luo; Jing He; Xiulan Ma; Junxiong Wen; Danhong Qiu; Guohui Nie; Rizhao Liu; Guohua Hu; Tao Chen
Journal:  J Med Internet Res       Date:  2022-08-03       Impact factor: 7.076

  3 in total

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