Variable selection and regression analysis for the prediction of mortality rates associated with foodborne diseases.

ABSTRACT: The purpose of this study was to apply a novel statistical method for variable selection and a model-based approach for filling data gaps in mortality rates associated with foodborne diseases using the WHO Vital Registration mortality dataset. Correlation analysis and elastic net regularization methods were applied to drop redundant variables and to select the most meaningful subset of predictors. Whenever predictor data were missing, multiple imputation was used to fill in plausible values. Cluster analysis was applied to identify similar groups of countries based on the values of the predictors. Finally, a Bayesian hierarchical regression model was fit to the final dataset for predicting mortality rates. From 113 potential predictors, 32 were retained after correlation analysis. Out of t

SUBMITTER: Amene E 

PROVIDER: S-EPMC9150614 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature