Title: Deriving Enhanced Geographical Representations via Similarity-based Spectral Analysis: Predicting Colorectal Cancer Survival Curves in Iowa,
Abstract: Neural networks are capable of learning rich, nonlinear feature
representations shown to be beneficial in many predictive tasks. In this work,
we use such models to explore different geographical feature representations in
the context of predicting colorectal cancer survival curves for patients in the
state of Iowa, spanning the years 1989 to 2013. Specifically, we compare model
performance using "area between the curves" (ABC) to assess (a) whether
survival curves can be reasonably predicted for colorectal cancer patients in
the state of Iowa, (b) whether geographical features improve predictive
performance, (c) whether a simple binary representation, or a richer, spectral
analysis-elicited representation perform better, and (d) whether spectral
analysis-based representations can be improved upon by leveraging
geographically-descriptive features. In exploring (d), we devise a
similarity-based spectral analysis procedure, which allows for the combination
of geographically relational and geographically descriptive features. Our
findings suggest that survival curves can be reasonably estimated on average,
with predictive performance deviating at the five-year survival mark among all
models. We also find that geographical features improve predictive performance,
and that better performance is obtained using richer, spectral
analysis-elicited features. Furthermore, we find that similarity-based spectral
analysis-elicited representations improve upon the original spectral analysis
results by approximately 40%.