In this research, an accurate measurement system of diabetic retinopathy is developed and investigated using data mining techniques. Diabetic retinopathy is an injury produced in the blood vessels of retina because of diabetes. Such a disease leads to a loss of in a patient's vision. Hence, an early analysis of diabetic retinopathy using an accurate and fast technique provides the patient with enough protection treatment time. The color fund us image can be used to automatically detect and realize the various lesions of diabetic retinopathy and its normal features, respectively. The specifications of the normal color fund us images are analyzed and classified by the extraction method into normal or abnormal. Therefore, the abnormal image will then be categorized into three levels: Mild, moderate, and Severe. To predict the unknown class, an association rule and SVM classifier are used. The results are promising to support the patients and the accelerating process. They further hope to overcome many problems in this field and in any future research.