Cluster analysis for diabetic retinopathy prediction using data mining techniques

Diabetic retinopathy is a one of the increasing medical situation occurs due to fluctuating insulin level in the blood that leads to loss of vision. It is an ophthalmic disease which is mainly occurs due to the generation of the new abnormal blood vessels. Diabetic retinopathy with exudates are causing main health problem that leads to loss of sight. Patient suffering from diabetes are advised to undergo continual retinal test by reason of diabetic retinopathy. As the population is quite large as compared to healthcare system available, tests should be optimised and identification of the disease is complex and time consuming task. In this paper, clustering technique is used among the various data mining techniques, clustering is the good approach to handle the complex task. Experiment is conducted to identify the best clustering technique which can easily identify the various impacting factors of DR in less complex way. The experimental results reflect that the performance of K-means is better than other clustering techniques. This analysis will help the medical practitioner to identify best algorithm for disease detection and provide preventive measures in advance.