Vision-threatening pathological myopia presents several lesions affecting various retinal anatomical structures. Detection approaches, however, either focus on one anatomical feature or are not intentional. This study uses hypergraph learning to modulate delineated retinal anatomical features from fundus images and capitalize on hidden associations between them. Experiments are conducted to assess prediction performance when targeting a particular anatomical trait versus using a mixture of select anatomical features, and in comparison to a ResNet34-based convolutional neural network classifier. Results indicate better prediction with hypergraph learning on a mix of the delineated features (F1 score 89.75%, AUC score 95.39%). A choroid tessellation segmentation method is also included.