Predictive Fluid Eye Analysis via Machine Learning

Abstract

The human eye’s fluid dynamics play a vital role in diagnosing diseases like glaucoma, diabetic retinopathy, and dry eye syndrome. Traditional diagnostic methods are often invasive and timeconsuming. This research introduces a Predictive Fluid Eye Analysis system using Machine Learning (ML) to provide a non-invasive, efficient, and accurate diagnostic tool. The system utilizes image processing and deep learning to analyze fluid distribution, flow patterns, and abnormalities in ocular images. By training predictive models on eye image datasets, it can detect early disease signs, classify conditions, and recommend preventive measures. Techniques like convolutional neural networks (CNNs), support vector machines (SVMs), and decision trees enhance accuracy and reliability. This AI-driven approach minimizes dependence on manual diagnosis, offering a faster and cost-effective alternative for healthcare professionals. Early detection improves treatment outcomes, reduces vision loss, and enhances patient care. Integrating ML into ophthalmology can revolutionize eye disease detection, providing real-time, data-driven insights. As AI continues to evolve, predictive fluid eye analysis has the potential to become a fundamental tool in modern ophthalmology, ensuring better vision health and improved disease management worldwide.