Use of a deep learning and random forest approach to track changes in the predictive nature of socioeconomic drivers of under-5 mortality rates in sub-Saharan Africa.

Journal: BMJ open

PMID: 35177443

Abstract

OBJECTIVES: We used machine learning algorithms to track how the ranks of importance and the survival outcome of four socioeconomic determinants (place of residence, mother's level of education, wealth index and sex of the child) of under-5 mortality rate (U5MR) in sub-Saharan Africa have evolved.

Authors

Justine B Nasejje

School of Statistics and Actuarial Science, University of the Witwatersrand, Johannesburg, Gauteng, South Africa.
Rendani Mbuvha

Statistics and Actuarial Science, University of the Witwatersrand, Johannesburg-Braamfontein, South Africa.
Henry Mwambi

School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Pietermaritzburg, Private Bag X01, Scottsville, 3209, South Africa.

Keywords

Child Child Mortality Cross-Sectional Studies Deep Learning Ghana Humans Socioeconomic Factors

External Resources

View on PubMed Access via DOI PubMed (35177443)

Use of a deep learning and random forest approach to track changes in the predictive nature of socioeconomic drivers of under-5 mortality rates in sub-Saharan Africa.

Abstract

Authors

Keywords

External Resources

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