Interactive Diabetes Risk Prediction Using Explainable Machine Learning: A Dash-Based Approach with SHAP, LIME, and Comorbidity Insights
This provides an interactive tool for health awareness, but it is incremental as it applies existing methods to a specific dataset.
The study developed a web-based tool to predict diabetes risk using machine learning on the 2015 CDC BRFSS dataset, with LightGBM and undersampling achieving the best recall for risk detection.
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.