Literature DB >> 11677741

Classification of patients on the basis of otoneurological data by using Kohonen networks.

M Juhola1, J Laurikkala, K Viikki, E Kentala, I Pyykkö.   

Abstract

Machine learning methods such as neural networks, decision trees and genetic algorithms can be useful to aid in the classification of patients. We tested Kohonen artificial neural networks, which are known to be effective for classification tasks. Our sample included patients with six different diseases. The Kohonen network algorithm recognized the four largest groups reliably, but the two smallest groups were too small for the method. Neural networks seem to be promising for the computer-aided classification of otoneurological patients provided that the number of patients used is sufficiently large.

Entities:  

Mesh:

Year:  2001        PMID: 11677741     DOI: 10.1080/000164801750388108

Source DB:  PubMed          Journal:  Acta Otolaryngol Suppl        ISSN: 0365-5237


  3 in total

1.  Predictive capability of historical data for diagnosis of dizziness.

Authors:  Jeff G Zhao; Jay F Piccirillo; Edward L Spitznagel; Dorina Kallogjeri; Joel A Goebel
Journal:  Otol Neurotol       Date:  2011-02       Impact factor: 2.311

2.  Classification and clustering analysis of pyruvate dehydrogenase enzyme based on their physicochemical properties.

Authors:  Amit Kumar Banerjee; Sunita M; Naveen M; Upadhyayula Suryanarayana Murty
Journal:  Bioinformation       Date:  2010-04-30

3.  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

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.