Evolutionary methods for variable selection in the epidemiological modeling of cardiovascular diseases : научное издание | Научно-инновационный портал СФУ

Evolutionary methods for variable selection in the epidemiological modeling of cardiovascular diseases : научное издание

Тип публикации: статья из журнала

Год издания: 2018

Идентификатор DOI: 10.1186/s13040-018-0180-x

Аннотация: Background: The redundancy of information is becoming a critical issue for epidemiologists. High-dimensional datasets require new effective variable selection methods to be developed. This study implements an advanced evolutionary variable selection method which is applied for cardiovascular predictive modeling. The epidemiological follow-up study KIHD (Kuopio Ischemic Heart Disease Risk Factor Study) was used to compare the designed variable selection method based on an evolutionary search with conventional stepwise selection. The sample contains in total 433 predictor variables and a response variable indicating incidents of cardiovascular diseases for 1465 study subjects. Results: The effectiveness of variable selection methods was investigated in combination with two models: Generalized Linear Logistic Regression and Support Vector Machine. We managed to decrease the number of variables from 433 to 38 and save the predictive ability of the models used. Their performance was evaluated with an F-score metric. At most, we gained 65.6% and 67.4% of the F-score before and after variable selection respectively. All the results were averaged over 5-folds of a cross-validation procedure. Conclusions: The presented evolutionary variable selection method allows a reduced set of variables to be chosen which are relevant to predicting cardiovascular diseases. A reference list of the most meaningful variables is introduced to be used as a basis for new epidemiological studies. In general, the multicollinearity of variables enables different combinations of predictors to be used and the same performance of models to be attained.

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Издание

Журнал: BioData Mining

Выпуск журнала: Т. 11, 1

Номера страниц: 18

ISSN журнала: 17560381

Авторы

  • Brester C. (Institute of Computer Science and Telecommunications,Reshetnev Siberian State University of Science and Technology)
  • Rönkkö M. (Department of Environmental and Biological Sciences,University of Eastern Finland)
  • Kolehmainen M. (Department of Environmental and Biological Sciences,University of Eastern Finland)
  • Semenkin E. (Institute of Computer Science and Telecommunications,Reshetnev Siberian State University of Science and Technology)
  • Kauhanen J. (Institute of Public Health and Clinical Nutrition,University of Eastern Finland)
  • Tuomainen T.P. (Institute of Public Health and Clinical Nutrition,University of Eastern Finland)
  • Voutilainen S. (Institute of Public Health and Clinical Nutrition,University of Eastern Finland)
  • Ronkainen K. (Institute of Public Health and Clinical Nutrition,University of Eastern Finland)

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