Learning ensemble classifiers for diabetic retinopathy assessment

•Two ensemble classifiers are proposed for the diagnosis of diabetic retinopathy.•Classifiers are based on medical attributes available in the health care record.•Methods generate linguistic rules with two approaches: fuzzy and rough sets.•The best achieved accuracy is 84%.•Using these classifiers for decision support may avoid unnecessary medical tests. 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.