What question did this study set out to answer?

The research aims to evaluate various machine learning techniques for the detection and prediction of glaucoma.

February 5, 2026

Evaluation of machine learning techniques for glaucoma recognition and prediction

Key Points

The research aims to evaluate various machine learning techniques for the detection and prediction of glaucoma.
Conducted a literature review on automated glaucoma detection methods.
Analyzed preprocessing techniques and feature selection for machine learning.
Explored data sets used for training and testing these models.
Machine learning methods can identify 85 percent of glaucoma patients accurately.
Automated glaucoma prediction shows progress, particularly with Optical Coherence Tomography.

Abstract

AbstractGlaucoma is a quiet vision thief. Early detection of glaucoma is almost difficult, and there is currently no treatment for glaucoma in its latter stages. This research looked at a variety of automated glaucoma detection methods. A thorough review of the literature was performed on preprocessing, feature extraction, feature selection, Machine Learning methods, and data sets utilized for testing and training. Automated glaucoma prediction is critical, but sadly, only a small amount of work has been done in this area, and only a minimal level of accuracy has been reached. However, automated glaucoma detection has progressed to the point that most machine learning methods can correctly identify 85 percent of glaucoma patients. Glaucoma can be predicted successfully using Optical Coherence Tomography.

Evaluation of machine learning techniques for glaucoma recognition and prediction

Key Points

Abstract

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