Single Nucleotide Polymorphism relevance learning with Random Forests for Type 2 diabetes risk prediction.

Journal: Artificial intelligence in medicine
Published Date: Sep 22, 2017

Abstract

OBJECTIVE: The use of artificial intelligence techniques to find out which Single Nucleotide Polymorphisms (SNPs) promote the development of a disease is one of the features of medical research, as such techniques may potentially aid early diagnosis and help in the prescription of preventive measures. In particular, the aim is to help physicians to identify the relevant SNPs related to Type 2 diabetes, and to build a decision-support tool for risk prediction.

Authors

  • Beatriz López
    University of Girona, Campus Montilivi, building EPS4, 17071 Girona, Spain. Electronic address: beatriz.lopez@udg.edu.
  • Ferran Torrent-Fontbona
    University of Girona, Campus Montilivi, building EPS4, 17071 Girona, Spain. Electronic address: ferran.torrent@udg.edu.
  • Ramón Viñas
    University of Girona, Campus Montilivi, building EPS4, 17071 Girona, Spain. Electronic address: rvinast@gmail.com.
  • José Manuel Fernández-Real
    Biomedical Research Institute of Girona, Avda. de França, s/n, 17007 Girona, Spain; CIBERobn Pathophysiology of Obesity and Nutrition, Instituto de Salud Carlos III, Madrid, Spain. Electronic address: jmfreal@idibgi.org.

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