An Improved Approach for Detection of Diabetic Retinopathy Using Feature Importance and Machine Learning Algorithms

Diabetic Retinopathy is a human eye disease that causes damage to the eye's retina and may ultimately result in complete blindness. Early detection of diabetic retinopathy is needed to avoid complete blindness. Physical tests, such as visual acuity test, dilation of pupils, optical consistency tomography, is used to detect diabetic retinopathy. However, it is costly in terms of time and might affect the patients. In these consequences, this paper detects the presence of Diabetic Retinopathy in the human eye using a machine learning algorithm. The proposed method applies classification algorithms on several features (e.g., optical disk diameter, lesion-specific (microaneurysms, exudates) or presence of hemorrhages) of an existing Diabetic Retinopathy dataset. Then the features were extracted and used for the final decision making to predict the presence of diabetic retinopathy. The proposed system used Decision Tree, Logistic Regression. Support Vector Machine for the prediction. The proposed method achieved 88% accurate results which is much better than the existing works. Moreover, the proposed method achieves a better score in precision and recall which are 97% and 92%, respectively compared to the existing result 72% and 63%, i.e., more the 25% in each category on average which proves the enormousness of the proposed method.