  Female breast cancer (FBC) incidence rate (IR) varies greatly by counties
across the United States (US). Factors responsible for such high spatial
disparities are not well understood, making it challenging to design effective
intervention strategies. We predicted FBC IRs using prevailing machine learning
techniques for 1,754 US counties with a female population over 10,000. Outlier
counties with the unexpectedly high or low FBC IRs were identified by
controlling the non-modifiable factors (demographics and socioeconomics).
Impacts of the modifiable factors (lifestyle, healthcare accessibility, and
environment) were mapped. Our study also shed light on hidden FBC risk factors
at the regional scale. Methods developed in our study may be used to discover
the place-specific, population-level, modifiable factors for the intervention
of other types of cancer or chronic diseases.
