Diabetic: 11.7z

Analyze how patient health degrades or improves over the 11 recorded phases.

Helping hospitals prioritize screenings for patients whose "Diabetic 11" profiles show rapid metabolic decline. 5. Proposed Visualization 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. Analyze how patient health degrades or improves over

Utilizing k-fold cross-validation specifically designed for longitudinal healthcare data to prevent data leakage. 4. Potential Findings & Impact Diabetic 11.7z

Providing a tool for clinicians to identify high-risk patients 24 months before clinical symptoms manifest.