Application of techniques in the field of medicine has been an area of intense research in recent years. Diabetic Retinopathy and Glaucoma are two retinal diseases that are a major cause of blindness. Regular Screening for disease detection has been a highly labor – and resource- intensive task. Hence automatic detection of these diseases through techniques would be a great remedy. A novel computational approach for automatic disease detection is proposed that utilizes retinal image analysis and data mining techniques to accurately categorize the retinal images as Normal, Diabetic Retinopathy and Glaucoma caused. Three feature apposite and sixteen classification Algorithms were analyzed and used to identify the contributing features that gave better establish results. Our results prove that C4.5 and random tree classification techniques generate the maximum multi-class categorization training precision of 100% in classifying 45 images from the Gold Standard Database. Moreover the Fisher’s Ratio algorithm expose the most minimal and perfect set of portentous features on the retinal image data.