Comparing Naive Bayes and Decision Tree Techniques for Predicting the Risk of Diabetic Retinopathy

Classifying data is a common task in Machine learning. Data mining in health care is an emerging field of high importance for providing prognosis and a deeper understanding of medical data. Most data mining methods depend on a set of features that define the behaviour of the learning algorithm and directly or indirectly influence the complexity of resulting models. Diabetic retinopathy the most common diabetic eye disease, is caused by complications that occurs when blood vessels in the retina weakens or distracted. We have applied machine learning methods to predict the early detection of eye disease diabetic retinopathy and found that Decision Tree method to be 90% accurate. The performance was also measured by sensitivity and specificity.