Regression Trees and Random forest based feature selection for malaria risk exposure prediction
This work addresses malaria risk exposure prediction for public health applications, but it is incremental as it applies existing machine learning methods to a specific domain problem.
The paper tackled malaria risk prediction by selecting environmental and climate variables using regression trees and random forests with cross-validation, resulting in qualitatively better performance in selection, prediction, and CPU time compared to the GLM-Lasso method.
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.