A technique is proposed that can be used to predict the cup-to-disc ratio from a single optic fundus image and determine which image features have the highest contribution to a specific ophthalmologist’s measured cup-to-disc ratio. The procedure starts with image pre-processing. The main step of the procedure is feature extraction where image features related to pixel intensities are found. These features are used to train three different classifiers: neural networks, support vector machines, and sparse representation classifiers. The classifiers are tested and evaluated to see how accurately they can predict the cup-to-disc ratio. The best obtained results are in the 70-75% success range. Finally, feature ranking is performed using the methods of chi square and information gain on a combined feature vector using measured cup-to-disc ratios from each ophthalmologist to determine the importance and contribution of each feature to that ophthalmologist.