1. Abstract
Creating "delta" features that represent the change in health markers between the 11 recorded points. Diabetic 11.7z
This paper investigates the efficacy of various deep learning architectures in predicting the onset and progression of diabetic complications using the "Diabetic 11" longitudinal dataset. By integrating demographic, clinical, and biochemical markers over 11 distinct time intervals or patient clusters, we propose a novel transformer-based model that outperforms traditional RNNs in early risk detection. Diabetic 11.7z