Hybrid of Support Vector Machine Algorithm and K-Nearest Neighbor Algorithm to Optimize the Diagnosis of Eye Disease

In order to optimize the diagnosis of eye disease, the authors want to propose the combination of Support Vector Machine and K-Nearest Neighbor Algorithm to Optimize Diagnosis of Eye Disease. This is a combination method of SVM to get a better classification to diagnose the eye disease. The problem is how to get a better classification to diagnose eye disease. In this research, the hybrid of SVM-KNN is more accurate to classify the eye disease than SVM itself. The accuracy using the combination SVM-KNN algorithm is 94.67%.