Applications of Bayesian shrinkage prior models in clinical research with categorical responses.

ABSTRACT:

Background

Prediction and classification algorithms are commonly used in clinical research for identifying patients susceptible to clinical conditions such as diabetes, colon cancer, and Alzheimer's disease. Developing accurate prediction and classification methods benefits personalized medicine. Building an excellent predictive model involves selecting the features that are most significantly associated with the outcome. These features can include several biological and demographic characteristics, such as genomic biomarkers and health history. Such variable selection becomes challenging when the number of potential predictors is large. Bayesian shrinkage models have emerged as popular and flexible methods of variable selection in regression settings. This work discusses variable

SUBMITTER: Bhattacharyya A 

PROVIDER: S-EPMC9046716 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature