Learning ensemble classifiers for diabetic retinopathy assessment.

Journal: Artificial intelligence in medicine
Published Date: Oct 6, 2017

Abstract

Diabetic retinopathy is one of the most common comorbidities of diabetes. Unfortunately, the recommended annual screening of the eye fundus of diabetic patients is too resource-consuming. Therefore, it is necessary to develop tools that may help doctors to determine the risk of each patient to attain this condition, so that patients with a low risk may be screened less frequently and the use of resources can be improved. This paper explores the use of two kinds of ensemble classifiers learned from data: fuzzy random forest and dominance-based rough set balanced rule ensemble. These classifiers use a small set of attributes which represent main risk factors to determine whether a patient is in risk of developing diabetic retinopathy. The levels of specificity and sensitivity obtained in the presented study are over 80%. This study is thus a first successful step towards the construction of a personalized decision support system that could help physicians in daily clinical practice.

Authors

  • Emran Saleh
    Departament d'Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, Tarragona, Spain. Electronic address: emran.saleh@urv.cat.
  • Jerzy Błaszczyński
    Institute of Computing Sciences, Poznań University of Technology, 60-965 Poznań, Poland. Electronic address: jerzy.blaszczynski@cs.put.poznan.pl.
  • Antonio Moreno
    Department of Computer Science and Mathematics, ITAKA Research Group, Universitat Rovira i Virgili, Avda. Països Catalans, 26, 43007, Tarragona, Catalonia (Spain). antonio.moreno@urv.cat.
  • Aida Valls
    Departament d'Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, Tarragona, Spain. Electronic address: aida.valls@urv.cat.
  • Pedro Romero-Aroca
    Ophthalmic Service, University Hospital Sant Joan de Reus, Institut d'Investigació Sanitària Pere Virgili (IISPV), Universitat Rovira i Virgili, Reus (Tarragona), Spain. Electronic address: pedro.romero@urv.cat.
  • Sofia de la Riva-Fernández
    Ophthalmic Service, University Hospital Sant Joan de Reus, Institut d'Investigació Sanitària Pere Virgili (IISPV), Universitat Rovira i Virgili, Reus (Tarragona), Spain. Electronic address: delariva.sofia@gmail.com.
  • Roman Słowiński
    Institute of Computing Sciences, Poznań University of Technology, 60-965 Poznań, Poland; Systems Research Institute, Polish Academy of Sciences, 01-447 Warsaw, Poland. Electronic address: roman.slowinski@cs.put.poznan.pl.

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