Journal
ARTIFICIAL INTELLIGENCE IN MEDICINE
Volume 85, Issue -, Pages 50-63Publisher
ELSEVIER
DOI: 10.1016/j.artmed.2017.09.006
Keywords
Diabetic retinopathy; Decision support systems; Rule-based models; Fuzzy decision trees; Random forest; Ensemble classifiers; Dominance-based rough set approach; Class imbalance
Funding
- Univ. Rovira i Virgili
- Institute de Salud Carlos III [PI15/01150, PI12/01535]
- URV grants [2015PFR-URV-B2-60, 2016PFR-URV-B2-60]
- Marti-Franques URV research fellowship programme [2016PMF-PIPF-24]
- Polish National Science Center [DEC-2013/11/B/ST6/00963]
- FEDER funds [PI15/01150, PI12/01535]
Ask authors/readers for more resources
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. (C) 2017 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available