CLASSIFICATION OF HEALTHY AND DIABETIC PATIENT BASED UPON EYE’S RETINA HEALTH CONDITIONS USING MACHINE LEARNING
Diabetes has become a global and common disease. There are numerous effects of this disease. Its effect can leads to complete blindness if proper medical precautions are not taken timely. The objective of this research is to classify the patient whether the patient is diabetic or not based upon the training of the model by binary classification of Diabetic Retinopathy (DR). We used available dataset that consists of many eye retina images. A hybrid methodology used in this research that consists of histograms and extraction of first and second order feathers. We investigated a lot of difference between the histograms of normal retina of eye than the affected eyes. The researchers generated histograms of the eye retina images, fetch its first order (mean, median, variance etc. ) and second order features (energy, entropy, homogeneity and contrast etc.). We achieved 0.814 precision, 0.821 accuracy, 0.821 and 0.817 f1 score using decision tree. Hence it can be used effectively for identifying DR and early detection of diabetic patient.
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