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.
Highlights
The commonest cause of blindness among working class is Diabetic Retinopathy which often leads to the complete loss of vision[1]
The World Health Organization (WHO) has estimated that Diabetic Retinopathy is responsible for 4.8% of the 37 million cases of blindness throughout the world
Image analysis tools can be used for automated detection of these various features and stages of Diabetes Retinopathy and can be referred to the specialist for intervention
Summary
The commonest cause of blindness among working class is Diabetic Retinopathy which often leads to the complete loss of vision[1]. Image analysis tools can be used for automated detection of these various features and stages of Diabetes Retinopathy and can be referred to the specialist for intervention Such tools will be useful for effective screening of Diabetic Retinopathy patients[3]. The results show that the most effective model to predict patients with heart diseases is naive Bayes (86.12%) followed by neural network and decision trees. It can incorporate other data mining techniques such as time series, clustering and association rules. Parthiban ‘Empirical Study on the Performance of Integrated Hybrid Prediction Model on the Medical Datasets’[8] system has been proposed to improve the diagnostic accuracy of diabetic disease by selecting informative features of Pima Indians Diabetes dataset. According to 15 data mining applications can be developed to evaluate the effectiveness of medical treatments
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