Detection of Dry Eyes Using Machine Learning

Dry Eye Syndrome is a prevalent ocular disorder that causes discomfort, blurred vision, and reduced quality of life for millions worldwide. Traditional diagnostic methods are expensive, subjective, and require specialized clinical training. This study proposes a machine learning (ML)-based system for automated dry eye detection. The model analyzes tear film characteristics, user symptoms, and environmental factors using structured questionnaire patterns to predict DES likelihood. The results demonstrate that the proposed system achieves high accuracy and provides a cost-effective, accessible alternative for early DES diagnosis, improving patient outcomes.