Exploring machine learning algorithms to predict acute respiratory tract infection and identify its determinants among children under five in Sub-Saharan Africa

According to this study, children who didn't take vaccinations had weakened immune systems and were highly affected by ARIs in Sub-Saharan Africa. The random forest machine learning model provides greater predictive power for estimating acute respiratory infections and identifying risk factors. This leads to a recommendation for policy direction to reduce infant mortality in Sub-Saharan Africa.

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Publication Details

Journal
Frontiers in Pediatrics
Published
2024-11-20
DOI
https://doi.org/10.3389/fped.2024.1388820
Citations
3
Primary Topic
COVID-19 diagnosis using AI
Type
article
Field-Weighted Citation Impact
1.21
Controls
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article

Exploring machine learning algorithms to predict acute respiratory tract infection and identify its determinants among children under five in Sub-Saharan Africa

Sisay Maru Wubante, Nebiyu Mekonnen Derseh, Bezawit Melak Fente, Tirualem Zeleke Yehuala
3 citations
Frontiers in Pediatrics
COVID-19 diagnosis using AI
1.21
article

Exploring machine learning algorithms to predict acute respiratory tract infection and identify its determinants among children under five in Sub-Saharan Africa

Sisay Maru Wubante, Nebiyu Mekonnen Derseh, Bezawit Melak Fente, Tirualem Zeleke Yehuala
article en
3 citations

Abstract

According to this study, children who didn't take vaccinations had weakened immune systems and were highly affected by ARIs in Sub-Saharan Africa. The random forest machine learning model provides greater predictive power for estimating acute respiratory infections and identifying risk factors. This leads to a recommendation for policy direction to reduce infant mortality in Sub-Saharan Africa.

Frontiers in PediatricsVol. 12
University of Gondar (ET)
Openalex Percentile: Top 19%
COVID-19 diagnosis using AI
1.21
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