Diabetic Retinopathy is one of the most familiar diseases and is a diabetes complication that affects eyes. Initially, diabetic retinopathy may cause no symptoms or only mild vision problems. Eventually, it can cause blindness. So early detection of symptoms could help to avoid blindness. In this paper, we present some experiments on some features of diabetic retinopathy, like properties of exudates, properties of blood vessels and properties of microaneurysm. Using the features, we can classify healthy, mild non-proliferative, moderate non-proliferative, severe non-proliferative and proliferative stages of DR. Support Vector Machine, Random Forest and Naive Bayes classifiers are used to classify the stages. Finally, Random Forest is found to be the best for higher accuracy, sensitivity and specificity of 76.5%, 77.2% and 93.3% respectively.

翻译：糖尿病视网膜病变是最常见的疾病之一，是一种影响眼睛的糖尿病并发症。初期，糖尿病视网膜病变可能无症状或仅引起轻微视力问题，但最终可能导致失明。因此，早期症状检测有助于避免失明。本文针对糖尿病视网膜病变的若干特征（如渗出物特性、血管特性及微动脉瘤特性）开展了一系列实验。利用这些特征，我们可以将糖尿病视网膜病变分为健康、轻度非增殖性、中度非增殖性、重度非增殖性及增殖性阶段。研究采用支持向量机、随机森林和朴素贝叶斯分类器进行阶段分类。最终发现，随机森林分类器在准确率、敏感性和特异性方面表现最优，分别达到76.5%、77.2%和93.3%。