Predicting poverty. Data mining approaches to the health and demographic surveillance system in Cuatro Santos, Nicaragua.

ABSTRACT:

Background

In order to further identify the needed interventions for continued poverty reduction in our study area Cuatro Santos, northern Nicaragua, we aimed to elucidate what predicts poverty, measured by the Unsatisfied Basic Need index. This analysis was done by using decision tree methodology applied to the Cuatro Santos health and demographic surveillance databases.

Methods

Using variables derived from the health and demographic surveillance update 2014, transferring individual data to the household level we used the decision tree framework Conditional Inference trees to predict the outcome "poverty" defined as two to four unsatisfied basic needs using the Unsatisfied Basic Need Index. We further validated the trees by applying Conditional random forest analyses in ord

SUBMITTER: Kallestal C 

PROVIDER: S-EPMC6819397 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature