Publications

Abstract : It is quite alarming that the increase of glaucoma is due to the lack of awareness of the disease and the cost for glaucoma screening. The primary eye care centers need to include a comprehensive glaucoma screening and include machine learning models to elaborate it as decision support system. The proposed system considers the state of art of eye gaze features to understand cognitive processing, direction and restriction of visual field. There is no significant difference in global and local ratio and skewness value of fixation duration and saccade amplitude, which suggest that there is no difference in cognitive processing. The significance value of saccadic extent along vertical axis, Horizontal Vertical ratio (HV ratio), convex hull area and saccadic direction show that there is restriction in vertical visual field. The statistical measures (p < 0.05) and Spearman correlation coefficient with class label validate the results. The proposed system compared the performance of seven classifiers: Naïve Bayes classifier, linear and kernel Support Vector classifiers, decision tree classifier, Adaboost, random forest and eXtreme Gradient Boosting (XGBoost) classifier. The discrimination of eye gaze features of glaucoma and normal is efficiently done by XGBoost with accuracy 1.0. The decision support system is cost-effective and portable.