An Examination of Data Mining Approaches in Glaucoma Identification

Abstract

Glaucoma is a genetic eye disorder that can lead to blindness. Early detection is challenging, requiring automated techniques like feature extraction and machine learning algorithms. However, these methods only identify the type of glaucoma. A thorough evaluation of over more than machine learning approaches, including support vector machine (SVM), K-means, and fuzzy c-means clustering algorithm, was conducted to identify the most accurate methods for detecting and predicting glaucoma. In this paper we have analysis of systematic review can identify the most reliable method for detecting and forecasting glaucoma, which can be used to improve future methods.