• DocumentCode
    3549365
  • Title

    Prediction of type II MODY3 diabetes using backpercolation

  • Author

    Khan, Nawaz ; Ikejiaku, Chukwuemeka A. ; Rahman, Shahedur

  • Author_Institution
    Middllesex Univ., London, UK
  • fYear
    2005
  • fDate
    23-24 June 2005
  • Firstpage
    401
  • Lastpage
    403
  • Abstract
    In this study, a neural network based approach is used to predict the presence of Maturity Onset Diabetes type 3, referred as MODY3 type II diabetes mellitus. The study has used backpercolation neural network algorithm to predict the specific genetic mutation that causes the MODY3 type II diabetes mellitus. A set of coded numeric values are assigned for numeric representation of genetic data that are available in public domain repositories. A point mutation is introduced in a portion of the nucleotide for the mutation prediction to train the data set. The study has demonstrated that backpercolation neural network algorithm is useful to train and to predict gene point mutation that leads to MODY3 type II diabetes.
  • Keywords
    diseases; feedforward neural nets; genetics; learning (artificial intelligence); medical computing; backpercolation; gene point mutation; genetic data; genetic mutation; maturity onset diabetes type 3; neural network algorithm; nucleotide; public domain repository; type II MODY3 diabetes mellitus; Computer errors; Computer networks; Diabetes; Diseases; Feedforward neural networks; Genetic mutations; Network topology; Neural networks; Neurons; Prediction algorithms;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer-Based Medical Systems, 2005. Proceedings. 18th IEEE Symposium on
  • ISSN
    1063-7125
  • Print_ISBN
    0-7695-2355-2
  • Type

    conf

  • DOI
    10.1109/CBMS.2005.85
  • Filename
    1467723