Applications of learning models for text-based datasets as well as image pixels-based datasets grow rapidly for prediction purposes. Pre-processing becomes challenging in carrying out image filtering and classifying. Retinal Fundus images plays important role in Diabetic Retinopathy (DR) diagnosis and treatment planning in various stages. Diabetic Retinopathy is diagnosed by observing the variation in retinal blood vessel, exudates, micro aneurysm, hemorrhages, and the new blood vessel growth inside the retina. The objective of this study is to enrich the diagnosis for the Diabetic Retinopathy from the retinal fundus images by applying machine learning algorithms. The proposed work implements normalization, parameter tuning, and optimal feature selection method to improve the classification accuracy offered by selected algorithms like decision tree algorithm and K-nearest neighborhood classifiers. The highest accuracy of 81.99%, Weighted Average of Receiver Operating Characteristics (ROC) 0.907 are obtained by k-Nearest Neighbor (KNN) classifier due to its best performance.

基于文本的数据集和基于图像像素的数据集的学习模型的应用在预测方面迅速增长。预处理在执行图像过滤和分类方面变得具有挑战性。视网膜眼底图像在糖尿病视网膜病变（DR）各阶段的诊断和治疗计划中起着重要作用。糖尿病视网膜病变是通过观察视网膜血管、渗出物、微动脉瘤、出血和视网膜内部新生血管的变化来诊断的。本研究的目的是通过应用机器学习算法从视网膜眼底图像丰富糖尿病性视网膜病变的诊断。拟议的工作实现标准化，参数调整，和最佳特征选择方法，以提高所选算法（如决策树算法和 K-最近邻分类器）提供的分类精度。由于其最佳性能，k-最近邻 (KNN) 分类器获得了 81.99% 的最高准确度，接收器操作特征 (ROC) 的加权平均值 (ROC) 0.907。