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 these 32 predictors, eight with non-zero coefficients were selected using the elastic net regularization method. Based on the values of these variables, four clusters of countries were identified. The uncertainty of predictions was large for countries within clusters lacking mortality rates, and it was low for a cluster that had mortality rate information. Our results demonstrated that, using Bayesian hierarchical regression models, a data-driven clustering of countries and a meaningful subset of predictors can be used to fill data gaps in foodborne disease mortality.

Machine learning to predict foodborne salmonellosis outbreaks based on genome characteristics and meteorological trends

“…An important consideration when utilizing large gene matrices in predictive modeling is identifying and selecting the smallest possible set of relevant genes that can help achieve good predictive performance, without model overfitting or including features that are irrelevant or redundant to the prediction process ( Guyon et al, 2003 ). In the presence of such data, it is important to employ a statistical approach to select meaningful subsets of predictors for samples with complete data, similar to the approach used by Amene et al (2016) to predict mortality rates associated with foodborne diseases. Elastic Net has previously proven to be effective (with an accuracy of >90%) in identifying genetic features of interest related to lung cancer ( Hughey and Butte, 2015 ).…”

A Tutorial on Supervised Machine Learning Variable Selection Methods for the Social and Health Sciences in R

“…The use of variable selection techniques can lead to more accurate predictions, reduce the computational cost of creating the model, and improve the parsimony of the model by eliminating redundant and irrelevant variables. For example, variable selection techniques have been used to build models pertaining to identifying exposure-outcome associations [1] as well as predicting mortality rates [2,3] , psychological strain in teachers [4] , and nomophobia [5] .…”

A Tutorial on Supervised Machine Learning Variable Selection Methods for the Social and Health Sciences in R

“…The use of variable selection techniques can lead to more accurate predictions, reduce the computational cost of creating the model, and improve the parsimony of the model by eliminating redundant and irrelevant variables. For example, variable selection techniques have been used to build models pertaining to identifying exposure-outcome associations [1] as well as predicting mortality rates [2,3] , psychological strain in teachers [4] , and nomophobia [5] .…”