Predicting healthcare costs using GAs

Authors:
C. R. Stephens;H. Waelbroeck;S. Talley
Affiliations:
Instituto de Ciencias, Nucleares, UNAM, México D.F.;eXa Inc., New York, NY;Adaptive Technologies Inc., Glendale, AZ
Venue:
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Year:
2005

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Abstract

Predicting prospective healthcare costs is of increasing importance. Genetic search is used to discover attribute sets and associated posterior probability classifiers that predict the top 0.5% most costly individuals in year N + 1 based on previous medical conditions and costs in year N. The predictive performance of single-variable classifiers (cost-drivers), found using statistical measures familiar from datamining, as well as Naive Bayesian analysis, are compared and contrasted with that of classifiers found using genetic search. Comparison is also made with two well known benchmarks from the healthcare literature.