SHAP Explainability
Cross-source consensus on SHAP Explainability from 1 sources and 5 claims.
1 sources · 5 claims
How it works
Benefits
Evidence quality
Highlighted claims
- Glucose, BMI, and Age were the dominant predictors in Random Forest SHAP attribution. — A Unified Three-Stage Machine Learning Framework for Diabetes Detection, Subtype Discrimination, and Cognitive-Metabolic Hypothesis Testing
- High glucose increased predicted diabetes probability. — A Unified Three-Stage Machine Learning Framework for Diabetes Detection, Subtype Discrimination, and Cognitive-Metabolic Hypothesis Testing
- SHAP was used to explain the strongest tree-ensemble diabetes model selected by cross-validated AUC. — A Unified Three-Stage Machine Learning Framework for Diabetes Detection, Subtype Discrimination, and Cognitive-Metabolic Hypothesis Testing
- BMI had a bimodal contribution in which low values were protective and elevated values increased risk. — A Unified Three-Stage Machine Learning Framework for Diabetes Detection, Subtype Discrimination, and Cognitive-Metabolic Hypothesis Testing
- Symptoms and some additional variables contributed little after glucose and BMI were available. — A Unified Three-Stage Machine Learning Framework for Diabetes Detection, Subtype Discrimination, and Cognitive-Metabolic Hypothesis Testing