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This study aims to predict multidimensional poverty in Tanzania and identify its key predictors. Understanding these factors is crucial for effective interventions.

June 13, 2026PeerJ Computer ScienceOpen Access

A hybrid AI factor framework for longitudinal analysis of multidimensional poverty status

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This study aims to predict multidimensional poverty in Tanzania and identify its key predictors. Understanding these factors is crucial for effective interventions.
Employed hybrid artificial intelligence techniques integrating factor analysis and k-means clustering.
Used longitudinal survey data from 2014/15 and 2020/21 across two analytical setups.
Evaluated extreme gradient boosting (XGBoosting) and generalized linear model (GLM) for performance.
GLM demonstrated an Area Under the Curve of 99.8%, with F1-scores of 98.3% for poor households and 96.9% for non-poor households.
XGBoosting outperformed other algorithms in the independent analysis of the first data wave.
Key predictors include factors such as household education, income diversification constraints, and livelihood shocks.

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Implication

Randomized trial predicts multidimensional poverty in Tanzania, indicating actionable insights for policymakers.

Cite This Study

Sende et al. (2026) studied this question.

synapsesocial.com/papers/6a2cf5d7faef96ed7f057c81 https://doi.org/10.7717/peerj-cs.3873

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A hybrid AI factor framework for longitudinal analysis of multidimensional poverty status

Key Points

Discussion

Implication

Cite This Study

A hybrid AI factor framework for longitudinal analysis of multidimensional poverty status

Key Points

Discussion

Implication

Cite This Study

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