SVM and statistical technique method applying in Primary Open Angle Glaucoma diagnosis

The Primary Open Angle Glaucoma(POAG) discriminated model using support vector machine(SVC) method is presented to distinguish the primary open-angle glaucoma disease, which is not clear in early symptoms and involves in various risk factors, moreover easy to blind with prolonged intraocular hypertension. Through case study of clinical diagnosis, SVM classifier with a radial basis inner function was established to predict and discriminate some unknown patients in the paper. At the same time, Bayes angle discriminated model and Logistic regression, which are traditional statistical classification approaches, are set up to compare with SVM methods in POAG diagnosis. In the end, we conclude that SVM method is reliable and superior in many respects to statistical classification methods in the POAG recognition.