Visualizing Type 2 Diabetes Prevalence: Localizing Model Feature Impacts

Abstract

SHAP values have been a common approach used to understand machine learning model predictions by averaging the marginal contributions of each feature across every possible permutation of the feature set. Our research provides a localized view of SHAP values contributing to Type 2 Diabetes (T2D) prevalence in the United States from 2012 - 2021 covering each year independently. Instead of visualizing SHAP feature importance across an entire geographical dataset using a beeswarm plot, our approach is more granular. We visualize individual SHAP values of Social Determinants of Health (SDOH) features by county on a Choropleth map. Additionally, we found that replacing geographic identifiers such as zipcode with precise latitude and longitude coordinates before applying KNN imputation reduced the MSE by 10%. Our visualization reveals how specific factors influence T2D prevalence at the county level using a non-linear machine learning model. By re-appending the initially preserved geographic identifiers for each record by index, we traced the contribution of each SHAP value back to its locality. Our approach opens up a new geographical vantage point of the mechanisms of model predictions, thereby identifying localized key factors influencing Type 2 Diabetes (T2D). This study extends the possibilities for tailored interventions and public health policies showing how some factors have varying predictive impact on an outcome at the geographic level.