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Neural networks are capable of learning rich, nonlinear featurerepresentations shown to be beneficial in many predictive tasks. In this work,we use these models to explore the use of geographical features in predictingcolorectal cancer survival curves for patients in the state of Iowa, spanningthe years 1989 to 2012. Specifically, we compare model performance using anewly defined metric -- area between the curves (ABC) -- to assess (a) whethersurvival curves can be reasonably predicted for colorectal cancer patients inthe state of Iowa, (b) whether geographical features improve predictiveperformance, and (c) whether a simple binary representation or richer, spectralclustering-based representation perform better. Our findings suggest thatsurvival curves can be reasonably estimated on average, with predictiveperformance deviating at the five-year survival mark. We also find thatgeographical features improve predictive performance, and that the bestperformance is obtained using richer, spectral analysis-elicited features.

Learning Rich Geographical Representations: Predicting Colorectal Cancer Survival in the State of Iowa