Table 4 The results of using a linear regression model to study the effect of multiple demographic, socio-economic, and other factors on overall cancer mortality in the University of Chicago Medicine Comprehensive Cancer Center service area.
From: Utilizing geospatial artificial intelligence to map cancer disparities across health regions
feature | Coefficients | Coeff (%) contribution | t.value | P.value | R2 adjusted |
|---|---|---|---|---|---|
Poverty_rate | 0.432 | 3.31 | 2.831 | 0.006 | 0.596 |
Teen_birth_rate | 0.398 | 30.5 | 2.817 | 0.006 | 0.596 |
Uninsured_rate | −0.238 | 18.3 | −2.016 | 0.048 | 0.596 |
Demographics_Hispanic_or_Latino | −0.175 | 13.4 | −1.423 | 0.159 | 0.596 |
Routine_checkup_rate | −0.023 | 1.7 | −0.257 | 0.798 | 0.596 |
Single_parent_households | 0.031 | 2.4 | 0.178 | 0.859 | 0.596 |
Demographics_Males | −0.007 | 0.6 | −0.061 | 0.952 | 0.596 |
