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Abstract: Diabetic Retinopathy is a disease that affects the eyes as a result of diabetes. This disorder is caused by damage to the blood vessels of the light-sensitive tissue in the retina of the eye . At beginning, diabetic retinopathy may appear normal or very moderate vision changes. It has the capacity to cause blindness. The number of doctors in India is fairly low in comparison to the number of patients, resulting in delayed disease identification. However, if diabetic retinopathy is not detected early enough, it can cause irreversible damage to the eyes, leading to full blindness. To avoid this, we decided to use machine learning to automate the diagnosis procedure. The ability of existing manual testing has been hampered by the rise in diabetes cases. Today, new algorithms for assisted diagnosis are critical. Diabetes can be detected early, which can aid patients and reduce negative health outcomes like blindness. For classification of the extracted histogram, we employ a sequential model technique. It is proposed that features be represented using a histogram binning approach. The testing findings reveal that utilizing sequential mode, LESH is the most accurate technique, with an accuracy of 0.904.