Screen Based Vision Acuity Prediction Using  Machine Learning and Failure Threshold Analysis

Author(s)	Mr. Sudheendra Kurakula, Ms. Thanmayi Rapolu
Country	India
Abstract	Sight or vision is one of the most vital functions that enable an individual to perceive his/her surroundings and interact effectively. One of the most prevalent eye conditions affecting millions of people across the globe is refractive errors such as myopia and hypermetropia. Identifying these eye problems as early as possible is necessary to help prevent further complications. Vision tests done traditionally may need some level of clinical facilities together with other factors. It becomes very difficult to achieve the same through conventional means. This project aims at providing a solution to these problems through the introduction of an online vision test tool that estimates eye power based on visual acuity. The proposed method involves displaying random characters at various sizes using a digital screen and then increasing or decreasing their complexity depending on the performance of a user.
Keywords	Vision acuity, adaptive testing, myopia, hypermetropia, screen-based vision testing, machine learning, Random Forest, failure threshold, vision prediction, graphical user interface.
Field	Medical / Pharmacy
Published In	Volume 8, Issue 3, May-June 2026
Published On	2026-05-23

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