Journal of Computational Physics
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
The art of computer programming, volume 1 (3rd ed.): fundamental algorithms
The art of computer programming, volume 1 (3rd ed.): fundamental algorithms
ISMDA '00 Proceedings of the First International Symposium on Medical Data Analysis
Journal of Biomedical Informatics - Special issue: Clinical machine learning
Bayesian projection approaches to variable selection in generalized linear models
Computational Statistics & Data Analysis
Feature subset selection by genetic algorithms and estimation of distribution algorithms
Artificial Intelligence in Medicine
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We use Bayesian decision theory to address a variable selection problem arising in attempts to indirectly measure the quality of hospital care, by comparing observed mortality rates to expected values based on patient sickness at admission. Our method weighs data collection costs against predictive accuracy to find an optimal subset of the available admission sickness variables. The approach involves maximizing expected utility across possible subsets, using Monte Carlo methods based on random division of the available data into N modeling and validation splits to approximate the expectation. After exploring the geometry of the solution space, we compare a variety of stochastic optimization methods –- including genetic algorithms (GA), simulated annealing (SA), tabu search (TS), threshold acceptance (TA), and messy simulated annealing (MSA) –- on their performance in finding good subsets of variables, and we clarify the role of N in the optimization. Preliminary results indicate that TS is somewhat better than TA and SA in this problem, with MSA and GA well behind the other three methods. Sensitivity analysis reveals broad stability of our conclusions.