Keratonalyse: A Study of Comparative Analysis of Supervised Learning Algorithms for Keratoconus Detection

Keratoconus is a corneal disorder that causes corneal thinning and also corneal bulging, with symptoms such as astigmatism, light sensitivity, blurred vision, etc. Eye is one of the compound organs in the human body, genetic, ecological, and age-related issues can affect your eyes. This can range from discomfort to more serious vision issues which may vandalize your eyesight. Early detection and diagnosis of keratoconus (screening) requires an in-depth corneal analysis using different types of techniques such as slip lamp analysis, corneal tomography. The project involves the use of machine learning algorithms to detect and classify keratoconus using data collected in the form of a csv file containing 3164 patient data with 447 parameters and the application of supervised learning. The accuracy results obtained were 90%.