  The transparent cornea is the window of the eye, facilitating the entry of
light rays and controlling focusing the movement of the light within the eye.
The cornea is critical, contributing to 75% of the refractive power of the eye.
Keratoconus is a progressive and multifactorial corneal degenerative disease
affecting 1 in 2000 individuals worldwide. Currently, there is no cure for
keratoconus other than corneal transplantation for advanced stage keratoconus
or corneal cross-linking, which can only halt KC progression. The ability to
accurately identify subtle KC or KC progression is of vital clinical
significance. To date, there has been little consensus on a useful model to
classify KC patients, which therefore inhibits the ability to predict disease
progression accurately.
  In this paper, we utilised machine learning to analyse data from 124 KC
patients, including topographical and clinical variables. Both supervised
multilayer perceptron and unsupervised variational autoencoder models were used
to classify KC patients with reference to the existing Amsler-Krumeich (A-K)
classification system. Both methods result in high accuracy, with the
unsupervised method showing better performance. The result showed that the
unsupervised method with a selection of 29 variables could be a powerful tool
to provide an automatic classification tool for clinicians. These outcomes
provide a platform for additional analysis for the progression and treatment of
keratoconus.
