As a guest user you are not logged in or recognized by your IP address. You have
access to the Front Matter, Abstracts, Author Index, Subject Index and the full
text of Open Access publications.
Diabetic retinopathy is an ophthalmic malady that is the major cause of blindness in diabetic patients. Early detection is important to minimize the risk of vision loss. An screening of the eye fundus can confirm the disease and its severity but this test is costly and time-consuming. In this work, we propose a decision support system that uses fuzzy random forests to analyze the clinical data of each patient in order to detect any sign of developing diabetic retinopathy and to determine the necessity of the screening. The combination of fuzzy sets and a classifier ensemble for the detection of diabetic retinopathy achieves high sensitivity and specificity scores, improving the results given when using a single decision tree.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.