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Health Informatics Journal
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The use of Cox regression and genetic algorithm (CoRGA) for identifying risk factors for mortality in older people

Rabiah Ahmad

Health Informatics Research Group, Department of Information Studies, University of Sheffield, Western Bank, Sheffield S10 2TN, UK, Lip00ra{at}sheffield.ac.uk

Peter A. Bath

Health Informatics Research Group, Department of Information Studies, University of Sheffield, Sheffield S10 2TN, UK, p.a.bath{at}sheffield.ac.uk

The increase in the proportion and number of older people in developed countries has resulted in research investigating risk factors for adverse health outcomes, including mortality. However, research has been limited by the range of risk factors included in regression models. This is partly because traditional statistical methods and software packages allow a restricted number of variables and combinations of variables. This article describes ongoing research to overcome these limitations through the CoRGA program, which combines Cox regression with a genetic algorithm for the variable selection process. CoRGA was used to try and identify the best combination of risk factors for 4-year all-cause mortality. The combination of 10 risk factors identified by CoRGA included both known and new risk factors for mortality in older people. Further research is seeking to develop the program further and to identify further risk factors for all-cause mortality in older people.

Key Words: Cox regression • genetic algorithm • mortality • risk factors • software package

Health Informatics Journal, Vol. 10, No. 3, 221-236 (2004)
DOI: 10.1177/1460458204042236
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