Machine learning models based on the dimensionality reduction of standard automated perimetry data for glaucoma diagnosis.

•A set of machine learning-based diagnostic models are designed that implement data manipulation, dimensionality reduction, and classification methods.•Structure-functional and anatomical knowledge are reflected via new input variables derived from visual field clustering schemes.•Dimensionality reduction is conducted to select important variables so as to alleviate high-dimensionality problems.•For comparison, we applied various visual field clustering schemes, dimensionality reduction techniques, and classifiers and obtained the best model giving an AUC of 0.912.