A tutorial on variable selection for clinical prediction models: Feature selection methods in data-mining could improve the results.

08:00 EDT 13th October 2015 | BioPortfolio

Summary of "A tutorial on variable selection for clinical prediction models: Feature selection methods in data-mining could improve the results."

Identifying an appropriate set of predictors for the outcome of interest is a major challenge in clinical prediction research. The aim of this study is to show the application of some variable selection methods, usually used in data-mining, for an epidemiological study. We introduce here a systematic approach.


Journal Details

This article was published in the following journal.

Name: Journal of clinical epidemiology
ISSN: 1878-5921


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Medical and Biotech [MESH] Definitions

Adverse of favorable selection bias exhibited by insurers or enrollees resulting in disproportionate enrollment of certain groups of people.

The selection or choice of sexual partner in animals. Often this reproductive preference is based on traits in the potential mate, such as coloration, size, or behavioral boldness. If the chosen ones are genetically different from the rejected ones, then NATURAL SELECTION is occurring.

The introduction of error due to systematic differences in the characteristics between those selected and those not selected for a given study. In sampling bias, error is the result of failure to ensure that all members of the reference population have a known chance of selection in the sample.

The techniques used to produce molecules exhibiting properties that conform to the demands of the experimenter. These techniques combine methods of generating structural changes with methods of selection. They are also used to examine proposed mechanisms of evolution under in vitro selection conditions.

Development of a library collection, including the determination and coordination of selection policy, assessment of needs of users and potential users, collection use studies, collection evaluation, identification of collection needs, selection of materials, planning for resource sharing, collection maintenance and weeding, and budgeting.

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