A three-stage ML framework achieves 0.825 ROC-AUC for diabetes detection, identifies two subtypes via clustering on glucose insulin and age, and finds a significant positive correlation (rho=0.208) between glycaemic control and cognitive function.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
A Unified Three-Stage Machine Learning Framework for Diabetes Detection, Subtype Discrimination, and Cognitive-Metabolic Hypothesis Testing
A three-stage ML framework achieves 0.825 ROC-AUC for diabetes detection, identifies two subtypes via clustering on glucose insulin and age, and finds a significant positive correlation (rho=0.208) between glycaemic control and cognitive function.