A LightGBM model trained on undersampled 2015 BRFSS data is wrapped in a Dash app with SHAP, LIME, and comorbidity insights for interactive diabetes risk prediction.
A comparison of instance-level counterfactual explanation algorithms for behavioral and textualdata:SEDC,LIME-CandSHAP-C,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Interactive Diabetes Risk Prediction Using Explainable Machine Learning: A Dash-Based Approach with SHAP, LIME, and Comorbidity Insights
A LightGBM model trained on undersampled 2015 BRFSS data is wrapped in a Dash app with SHAP, LIME, and comorbidity insights for interactive diabetes risk prediction.