Explore the factors related to the death of offspring under age five and appraise the hazard of child mortality using machine learning techniques in Bangladesh

The Gradient Boosting Machine and Random Forest produce the best predictive power for classifying child mortality and may help to ameliorate policy decision-making in this regard.

Authors

Ashikur Rahman (ORCID: https://orcid.org/0000-0003-4787-4920)
Md. Habibur Rahman (ORCID: https://orcid.org/0000-0003-3860-3678)

Institutions

Jahangirnagar University (BD)

Publication Details

Journal: BMC Public Health
Published: 2025-01-29
DOI: https://doi.org/10.1186/s12889-025-21460-w
Citations: 4
Primary Topic: Insurance, Mortality, Demography, Risk Management
Type: article
Field-Weighted Citation Impact: 22.37

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article

Explore the factors related to the death of offspring under age five and appraise the hazard of child mortality using machine learning techniques in Bangladesh

Ashikur Rahman, Md. Habibur Rahman

Jan 29, 2025

4 citations

BMC Public Health

Insurance, Mortality, Demography, Risk Management

22.37

article

Explore the factors related to the death of offspring under age five and appraise the hazard of child mortality using machine learning techniques in Bangladesh

Ashikur Rahman, Md. Habibur Rahman

January 29, 2025article en

4 citations

Abstract

The Gradient Boosting Machine and Random Forest produce the best predictive power for classifying child mortality and may help to ameliorate policy decision-making in this regard.

BMC Public HealthVol. 25(1)

Jahangirnagar University (BD)

Good health and well-being

Openalex Percentile: Top 1%

Insurance, Mortality, Demography, Risk Management

22.37

DOI Open Access PDF OpenAlex

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Explore the factors related to the death of offspring under age five and appraise the hazard of child mortality using machine learning techniques in Bangladesh — Ashikur Rahman, Md. Habibur Rahman · BMC Public Health (2025) | TGRS Research Map | TGRS