• DocumentCode
    3563699
  • Title

    A robust approach for classifying unknown data in medical diagnosis problems

  • Author

    Garc?­a-laencina, Pedro J. ; Vidal, Anibal R Figueiras ; Sancho-gomez, Jose-luis

  • Author_Institution
    Univ. Politec. de Cartagena, Cartagena
  • fYear
    2008
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    Unknown data is a common drawback in medical diagnosis applications. A recommended procedure for dealing with unknown values is missing data imputation, i.e., estimating and filling missing values using all the available information. This work* presents a robust approach for incomplete data classification using an enhanced version of the K Nearest Neighbours algorithm. Experimental results on medical diagnosis databases show the usefulness of this approach.
  • Keywords
    medical diagnostic computing; pattern classification; K Nearest Neighbours algorithm; incomplete data classification; medical diagnosis problems; missing data imputation; unknown values; Biomedical equipment; Databases; Filling; Machine learning; Machine learning algorithms; Medical diagnosis; Medical diagnostic imaging; Medical tests; Pattern classification; Robustness; Imputation; Medical diagnosis; Missing Data; Pattern Classification;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Automation Congress, 2008. WAC 2008. World
  • Print_ISBN
    978-1-889335-38-4
  • Electronic_ISBN
    978-1-889335-37-7
  • Type

    conf

  • Filename
    4699054
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3563699