Prediction of Chances - Diabetic Retinopathy Using Data Mining Classification Techniques

Abstract

Diabetic retinopathy the most common diabetic eye disease, is caused by complications that occurs when blood vessels in the retina weakens or distracted. It results in loss of vision if early detection is not done. Several data mining technique serves different purposes depending on the modeling objective. The outcome of the various data mining classification techniques was compared using rapid miner tool. We have used Naive bayes and Support Vector Machine to predict the early detection of eye disease diabetic retinopathy and found that Naive bayes method to be 83.37% accurate. The performance was also measured by sensitivity and specificity. The above methodology has also shown that our data mining helps to retrieve useful correlation even from attributes which are not direct indicators of the class which we are trying to predict.