Computer Aided Diagnosis Model of Glaucoma with Eye Tracking Data

Abstract

Computer Aided Diagnostics (CAD) can be considered as a ‘second opinion’, it is an important application of machine learning to help health professionals in making diagnostic decisions. CAD is a broad concept to assist medical professionals in decision making process, that has been applied to a variety of medical data integrated with image processing, computer vision, mathematics, physics, statistics and machine learning. Use of machine learning in computer aided diagnostics for Glaucoma is based on the fact that there are multiple tests available for the diagnosis, medical data are often not easily interpretable and the interpretation can depend very much on the skill of the doctor, specialization of medical fields that highly impact the accuracy in the diagnosis. Machine learning has models for understanding complex processes, statistical power to build more accurate predictive models. Increasing confidence in such diagnostics would decrease the number of patients or the complications associated with the disease. The only way to save vision due to glaucoma is to early detect its onset.  In the proposed work we have used dataset collected from an eye tracker for 55 participants (glaucoma patients and normal subjects) to develop a model for prediction of Glaucoma with logistic regression.