Use HMM and KNN for classifying corneal data
This work addresses the need for accurate classification systems in medical applications like ophthalmology, though it appears incremental by combining existing methods.
The researchers tackled the problem of classifying complex patterns in corneal topography data by developing a classifier using Hidden Markov Models (HMM) and K-Nearest Neighbors (KNN), achieving improved classification accuracy for Lasik data.
These days to gain classification system with high accuracy that can classify complicated pattern are so useful in medicine and industry. In this article a process for getting the best classifier for Lasik data is suggested. However at first it's been tried to find the best line and curve by this classifier in order to gain classifier fitting, and in the end by using the Markov method a classifier for topographies is gained.