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A hybrid AI factor framework for longitudinal analysis of Multidimensional Poverty Status

algorithms-and-analysis-of-algorithms
artificial-intelligence
data-mining-and-machine-learning
data-science
neural-networks

Abstract

Eliminating Multidimensional poverty in Tanzania remains a significant challenge, hindering the country’s progress toward sustained economic growth. Over 47% of households experience multidimensional poverty. Achieving the eradication of this problem by 2030, as Stated by the Sustainable Development Goals (SDG), requires empirically testable strategies and shared national commitment. This study employs hybrid AI techniques to analyse multidimensional poverty status using longitudinal data. The findings reveal that XGBoosting Classifier, outperforms other models tuned with H20AutoML and its other baseline models. Furthermore, household subsistence farming, households with no formal education, loss of crop at the household level, occupation of the household, household residential area and household tenure are the top important features influencing poverty. The study suggests that the government should adopt the application of the XGBoosting Classifier, which offers benefits to decision makers for poverty predictors that other algorithms could not cover to the extent that the XGBoosting Classifier achieves.
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