Regression Trees and Random forest based feature selection for malaria risk exposure prediction

TL;DR

This study employs regression trees and random forests to automatically select environmental and climate variables for predicting malaria vector exposure, resulting in improved prediction accuracy and efficiency over traditional methods.

Contribution

It introduces a novel variable selection approach using regression trees and random forests with stratified cross-validation for malaria risk prediction.

Findings

Better prediction accuracy than GLM-Lasso.

Reduced computational time.

Effective variable importance assessment.

Abstract

This paper deals with prediction of anopheles number, the main vector of malaria risk, using environmental and climate variables. The variables selection is based on an automatic machine learning method using regression trees, and random forests combined with stratified two levels cross validation. The minimum threshold of variables importance is accessed using the quadratic distance of variables importance while the optimal subset of selected variables is used to perform predictions. Finally the results revealed to be qualitatively better, at the selection, the prediction , and the CPU time point of view than those obtained by GLM-Lasso method.