Data-driven machine learning algorithm model for pneumonia prediction and determinant factor stratification among children aged 6–23 months in Ethiopia

Random forest is the best model to predict pneumonia and stratify its determinant factors. The implications of this study are profound for advanced research methodology, tailored to promote effective health interventions such as lifestyle modification and behavioral intervention, based on individuals' unique features, specifically for stakeholders to take proactive childcare interventions. The study would serve as pioneering evidence for future research, and researchers are recommended to use deep learning algorithms to enhance prediction accuracy.

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Publication Details

Journal
BMC Infectious Diseases
Published
2025-05-02
DOI
https://doi.org/10.1186/s12879-025-10916-4
Citations
5
Primary Topic
Pneumonia and Respiratory Infections
Type
article
Field-Weighted Citation Impact
6.77
Controls
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article

Data-driven machine learning algorithm model for pneumonia prediction and determinant factor stratification among children aged 6–23 months in Ethiopia

Addisalem Workie Demsash, Mulugeta Hayelom Kalayou, Shishir Purohit, Solomon Seyife Alemu et al.
5 citations
BMC Infectious Diseases
Pneumonia and Respiratory Infections
6.77
article

Data-driven machine learning algorithm model for pneumonia prediction and determinant factor stratification among children aged 6–23 months in Ethiopia

Addisalem Workie Demsash, Mulugeta Hayelom Kalayou, Shishir Purohit, Solomon Seyife Alemu, Mohammedamin Hajure, Geleta Nenko Dube, Rediet Abebe, Wubishet Gezimu, Firomsa Bekele, Michael Amera Tizazu, Lema Fikadu Wedajo, Gemeda Wakgari Kitil, Abera Lembebo
article en
5 citations

Abstract

Random forest is the best model to predict pneumonia and stratify its determinant factors. The implications of this study are profound for advanced research methodology, tailored to promote effective health interventions such as lifestyle modification and behavioral intervention, based on individuals' unique features, specifically for stakeholders to take proactive childcare interventions. The study would serve as pioneering evidence for future research, and researchers are recommended to use deep learning algorithms to enhance prediction accuracy.

BMC Infectious DiseasesVol. 25(1)
Akamai (United States) (US), Debre Berhan University (ET), Madda Walabu University (ET), Wollega University (ET), Azerbaijan State University of Economics (AZ), Ethiopian Civil Service University (ET), Wollo University (ET)
Zero hunger
Openalex Percentile: Top 3%
Pneumonia and Respiratory Infections
6.77
DOIOpen Access PDFOpenAlex
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Data-driven machine learning algorithm model for pneumonia prediction and determinant factor stratification among children aged 6–23 months in Ethiopia — Addisalem Workie Demsash, Mulugeta Hayelom Kalayou, et al. · BMC Infectious Diseases (2025) | TGRS Research Map | TGRS