Learning ensemble classifiers for diabetic retinopathy assessment

Identificador:  imarina:5131710

Handle: https://hdl.handle.net/20.500.11797/imarina5131710

Autors:  Saleh, Emran; Blaszczynski, Jerzy; Moreno, Antonio; Valls, Aida; Romero-Aroca, Pedro; de la Riya-Fernandez, Sofia; Slowinsk, Roman

Resum:
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.