Key result
Adding spatial data to random forest models boosts R² ~51% and eliminates residual spatial autocorrelation.
Why the study?
Standard machine learning models often fail to account for spatial dependence, limiting their ability to resolve local geographic mammography screening disparities.
Incorporating spatial neighborhood effects and geographic embeddings into random forest models substantially improves the prediction accuracy of local mammography screening rates.
Answers are grounded in this paper and cite their sources.
May refine geographic targeting of mammography outreach; hypothesis-generating for spatial ML in prevention research, pending validation.
e13643 Background: Mammography screening rates vary geographically across the United States, yet standard machine learning models often fail to account for spatial dependence, limiting their ability to resolve local screening disparities. This study examined whether incorporating spatial neighborhood effects and geographic embeddings improves random forest (RF) prediction accuracy and reduces residual spatial autocorrelation in tract-level screening estimates. Methods: PLACES 2024 tract-level mammography screening data were linked with socioeconomic and healthcare access indicators, including poverty, educational attainment, race, insurance coverage, median home value, proximity to mammography facilities, and the Social Vulnerability Index. Three RF regression models were trained using 5-fold cross-validation: (1) a base model with socioeconomic and access predictors; (2) a spatial-lag model including lag_mammo, defined as the mean screening rate of adjacent tracts; and (3) a spatial-embedded model adding geographic coordinate terms (longitude, latitude, longitude², latitude², and longitude×latitude). Model performance was evaluated using RMSE, MAE, R², and Moran’s I of residuals. Results: Incorporating spatial information improved performance across all metrics. The spatial-lag model reduced RMSE from 3.20 to 2.13 and increased R² from 0.55 to 0.80, eliminating residual spatial autocorrelation (Moran’s I = −0.01, p = 1.0). Adding coordinate embeddings further increased R² to 0.83. Conclusions: Incorporating spatial neighborhood effects and geographic embeddings substantially improved RF model performance and reduced spatial autocorrelation, supporting their use in identifying under-screened communities. This approach may assist in targeting interventions and informing strategies to reduce geographic disparities in preventive breast cancer care. Comparative predictive accuracy and spatial autocorrelation of three random forest models. Model RMSE MAE R² Moran’s I Base RF 3.20 2.50 0.55 +0.66 (p<0.01) Spatial-Lag RF 2.13 1.65 0.80 −0.01 (p=1.0) Spatial-Embedded RF 1.98 1.54 0.83 −0.046 (p=1.0) Negative Moran’s I values indicate no residual spatial dependence.
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Nketsiah et al. (2026) studied Mammography screening. Spatial-embedded random forest model vs. Base random forest model was evaluated on Model performance (R²). Incorporating spatial neighborhood effects and geographic embeddings into random forest models improved R² from 0.55 to 0.83 and eliminated residual spatial autocorrelation.
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