Bayesian probability

Feature selection

Mathematics

Statistic

Statistics

Machine learning

Akaike information criterion

Mathematical analysis

Artificial intelligence

Bayesian information criterion

Variable (mathematics)

Computer science

Model selection

Selection (genetic algorithm)

Family Medicine and Community Health

Clinical prediction models are used frequently in clinical practice to identify patients who are at risk of developing an adverse outcome so that preventive measures can be initiated. A prediction model can be developed in a number of ways; however, an appropriate variable selection strategy needs to be followed in all cases. Our purpose is to introduce readers to the concept of variable selection in prediction modelling, including the importance of variable selection and variable reduction strategies. We will discuss the various variable selection techniques that can be applied during prediction model building (backward elimination, forward selection, stepwise selection and all possible subset selection), and the stopping rule/selection criteria in variable selection (p values, Akaike information criterion, Bayesian information criterion and Mallows’ C p statistic). This paper focuses on the importance of including appropriate variables, following the proper steps, and adopting the proper methods when selecting variables for prediction models.

Variable selection strategies and its importance in clinical prediction modelling