The paper reports that SHAP-identified top features of deep learning models trained on BRFSS 2023 align with medical literature for 13 chronic diseases, and uses this alignment to argue the models are broadly trustworthy.
Interactive Diabetes Risk Prediction Using Explainable Machine Learning: A Dash-Based Approach with SHAP, LIME, and Comorbidity Insights
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abstract
This study presents a web-based interactive health risk prediction tool designed to assess diabetes risk using machine learning models. Built on the 2015 CDC BRFSS dataset, the study evaluates models including Logistic Regression, Random Forest, XGBoost, LightGBM, KNN, and Neural Networks under original, SMOTE, and undersampling strategies. LightGBM with undersampling achieved the best recall, making it ideal for risk detection. The tool integrates SHAP and LIME to explain predictions and highlights comorbidity correlations using Pearson analysis. A Dash-based UI enables user-friendly interaction with model predictions, personalized suggestions, and feature insights, supporting data-driven health awareness.
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Trustworthy Chronic Disease Risk Prediction For Self-Directed Preventive Care via Medical Literature Validation
The paper reports that SHAP-identified top features of deep learning models trained on BRFSS 2023 align with medical literature for 13 chronic diseases, and uses this alignment to argue the models are broadly trustworthy.