# Interactive Diabetes Risk Prediction Using Explainable Machine Learning: A Dash-Based Approach with SHAP, LIME, and Comorbidity Insights

arXiv: https://arxiv.org/abs/2505.05683 · HTML: https://mlsanity.com/paper/2505.05683/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 22 references that are in the map.
- First posted on arXiv 8 May 2025.

## Builds on (most cited first, 3 of 3)

- [Do Explanations Reflect Decisions? A Machine-centric Strategy to Quantify the Performance of Explainability Algorithms](https://mlsanity.com/paper/1910.07387/) (2019)
- [Counterfactual Explanation Algorithms for Behavioral and Textual Data](https://mlsanity.com/paper/1912.01819/) (2019)
- [A Comparative Study of Diabetes Prediction Based on Lifestyle Factors Using Machine Learning](https://mlsanity.com/paper/2503.04137/) (2025)

## Built on by (most cited first, 1 of 1)

- [Trustworthy Chronic Disease Risk Prediction For Self-Directed Preventive Care via Medical Literature Validation](https://mlsanity.com/paper/2506.17620/) (2025)

## Abstract (arXiv)

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.