Comparison of machine-learning algorithms to build a predictive model for detecting undiagnosed diabetes - ELSA-Brasil: accuracy study.

Journal: Sao Paulo medical journal = Revista paulista de medicina
Published Date: Jan 1, 2017

Abstract

CONTEXT AND OBJECTIVE:: Type 2 diabetes is a chronic disease associated with a wide range of serious health complications that have a major impact on overall health. The aims here were to develop and validate predictive models for detecting undiagnosed diabetes using data from the Longitudinal Study of Adult Health (ELSA-Brasil) and to compare the performance of different machine-learning algorithms in this task.

Authors

  • André Rodrigues Olivera
    MSc. IT Analyst, Postgraduate Computing Program, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre (RS), Brazil.
  • Valter Roesler
    PhD. Professor, Postgraduate Computing Program, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre (RS), Brazil.
  • Cirano Iochpe
    PhD. Professor, Postgraduate Computing Program, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre (RS), Brazil.
  • Maria Inês Schmidt
    PhD. Professor, Postgraduate Epidemiology Program and Hospital de Clínicas, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre (RS), Brazil.
  • Álvaro Vigo
    PhD. Professor, Postgraduate Epidemiology Program, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre (RS), Brazil.
  • Sandhi Maria Barreto
    PhD. Professor, Department of Social and Preventive Medicine & Postgraduate Program in Public Health, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte (MG), Brazil.
  • Bruce Bartholow Duncan
    PhD. Professor, Postgraduate Epidemiology Program and Hospital de Clínicas, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre (RS), Brazil.

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