Comparison of Various Data Mining Classification Techniques in the Diagnosis of Diabetic Retinopathy

Diabetic retinopathy (DR) has been the most frequently occurring complication in the patients suffering from a long-term diabetic condition, that ultimately leads to blindness. Early detection of the disease through biomarkers and effective treatment has been proposed to prevent/delay its occurrence. Several biomarkers have been explored, to help understand the incidence and progression of DR. These included the presence of microaneurysms, exudates, hemorrhages, etc. in the retina of the patients, which contributes to the disease. Investigation of the retinal images from time to time has been proposed as a strategy to prevent blindness. Evaluating the retinal images manually is time-consuming and demands great expertise in the diagnosis of DR. To circumvent such issues computer-aided diagnosis are very promising in the detection of DR. In the present study, we used a DR dataset and applied different classification algorithms in machine learning to predict the occurrence of the DR. The classifiers employed herein, included K-nearest neighbor, random forest classifier, support vector machine, regression tree classifier, logistic regression and the Naive Bayes theorem. Our results showed that the random forest classification model provided the significant detail of attributes in terms of their importance in the diagnosis of the DR. More importantly, our supervised classification models provided the prediction accuracy of the disease and Naive Bayes classifier demonstrated highest accuracy of 80.15% in the prediction of DR compared to the others. Additionally, receiver operating characteristics (ROC) analysis, with the classifiers and the area under curve (AUC) represented the fitting results of each classifier. The presented approach can prove to be a potential tool for the ophthalmologist in the early diagnosis tool for DR.