Machine Learning Models Based on the Dimensionality Reduction of Standard Automated Perimetry Data for Glaucoma Diagnosis

ConclusionA glaucoma diagnosis model giving an AUC of 0.912 was constructed by applying machine learning techniques to SAP data. The results show that dimensionality reduction not only reduces dimensions of the input space but also enhances the classification performance. The variable selection results show that the proposed composite variables from visual field clustering play a key role in the diagnosis model.
Source: Artificial Intelligence in Medicine - Category: Bioinformatics Source Type: research