Classification of the cause of eye impairment using different kinds of machine learning algorithms

Abstract

This study aims to create a machine learning-based method for categorizing ocular impairment. Congenital, refractive error, age, diabetes, and unknown are the five primary causes that specialists consider. The suggested technique automatically classifies patients into one of the five groups based on their unique features by evaluating the ODIR dataset of patient records, which includes numerous demographic and clinical information, and utilizing machine learning algorithms. Most previous studies in this area have focused on classifying illnesses; hence, this study's main contribution is its innovative focus on categorizing the causes of eye disorders. To the best of our knowledge, no ocular dataset has a label that specifies the cause of eye disease. The classes of eye disease have been added by Ophthalmologists. Better patient outcomes and more effective use of healthcare resources can be achieved by increasing the precision of physicians' diagnoses and streamlining their decision-making. Compared to the other classification methods, the Quadratic SVM model has the highest accuracy of 71.3%.