Clinically aligned COPD severity prediction using ordinal neural networks

Chronic Obstructive Pulmonary Disease (COPD) requires accurate severity staging for treatment planning and prognosis. Machine learning models for COPD severity prediction typically treat Global Initiative for Chronic Obstructive Lung Disease (GOLD) stages as nominal categories, ignoring their natural ordering. We developed an ordinal neural network framework that explicitly models the ordered structure of GOLD stages (1–4) while learning from heterogeneous clinical datasets with differing featur