Analyze the Determinants of Malnutrition in Women and Prognosticate Nutritional Status: Insights from the Bangladesh Demographic Health Survey 2022

Md. Habibur Rahman; Estiyak Ahmed Turjo

doi:doi:10.11648/j.wjph.20251001.16

Research Article |

| Peer-Reviewed

Analyze the Determinants of Malnutrition in Women and Prognosticate Nutritional Status: Insights from the Bangladesh Demographic Health Survey 2022

Md. Habibur Rahman^*

, Estiyak Ahmed Turjo

Published in World Journal of Public Health (Volume 10, Issue 1)

Received: 23 January 2025 Accepted: 24 February 2025 Published: 5 March 2025

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Abstract

Malnutrition in women is a significant public health concern and it is a serious issue in Bangladesh. The Bangladesh Demographic Health Survey (BDHS) 2022 was utilized to identify risk variables for malnourished females and fit several machine learning-based approaches to assess their nutritional status. This study included 7972 female individuals of various locations and ages. A chi-square test with a 5% significance level was used to identify possible risk variables for malnutrition in women. Naive Bayes, CART, Logistic Regression, Random Forest, Support Vector Machine, AdaBoost, Extreme Gradient Boosting, and Multilayer Perceptron; these eight machine learning-based classifiers were used to predict malnutrition in women. Summary information revealed that 48.4% of the population analyzed in this study were malnourished women. The chi-square test revealed that fourteen variables were substantially linked with malnutrition in women. Multilayer Perceptron had the highest accuracy of 0.71 for training data but it showed poor performance for the test data set. In terms of efficiency metrics such as accuracy, kappa, and F₁ scores, Random Forest outperformed the others. In comparison to the other ML algorithms tested in this study, the Random Forest technique was a significantly effective machine learning-based technique for predicting women's malnutrition in Bangladesh. The proposed approach can help identify high-risk women for malnutrition, reducing the burden on the healthcare system.

Published in	World Journal of Public Health (Volume 10, Issue 1)
DOI	10.11648/j.wjph.20251001.16
Page(s)	40-60
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2025. Published by Science Publishing Group

Keywords

Malnutrition, Machine Learning, AdaBoost, Cross Validation, Bangladesh

References

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Rahman, M. H., Turjo, E. A. (2025). Analyze the Determinants of Malnutrition in Women and Prognosticate Nutritional Status: Insights from the Bangladesh Demographic Health Survey 2022. World Journal of Public Health, 10(1), 40-60. https://doi.org/10.11648/j.wjph.20251001.16

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Rahman, M. H.; Turjo, E. A. Analyze the Determinants of Malnutrition in Women and Prognosticate Nutritional Status: Insights from the Bangladesh Demographic Health Survey 2022. World J. Public Health 2025, 10(1), 40-60. doi: 10.11648/j.wjph.20251001.16

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Rahman MH, Turjo EA. Analyze the Determinants of Malnutrition in Women and Prognosticate Nutritional Status: Insights from the Bangladesh Demographic Health Survey 2022. World J Public Health. 2025;10(1):40-60. doi: 10.11648/j.wjph.20251001.16

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@article{10.11648/j.wjph.20251001.16,
  author = {Md. Habibur Rahman and Estiyak Ahmed Turjo},
  title = {Analyze the Determinants of Malnutrition in Women and Prognosticate Nutritional Status: Insights from the Bangladesh Demographic Health Survey 2022},
  journal = {World Journal of Public Health},
  volume = {10},
  number = {1},
  pages = {40-60},
  doi = {10.11648/j.wjph.20251001.16},
  url = {https://doi.org/10.11648/j.wjph.20251001.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.wjph.20251001.16},
  abstract = {Malnutrition in women is a significant public health concern and it is a serious issue in Bangladesh. The Bangladesh Demographic Health Survey (BDHS) 2022 was utilized to identify risk variables for malnourished females and fit several machine learning-based approaches to assess their nutritional status. This study included 7972 female individuals of various locations and ages. A chi-square test with a 5% significance level was used to identify possible risk variables for malnutrition in women. Naive Bayes, CART, Logistic Regression, Random Forest, Support Vector Machine, AdaBoost, Extreme Gradient Boosting, and Multilayer Perceptron; these eight machine learning-based classifiers were used to predict malnutrition in women. Summary information revealed that 48.4% of the population analyzed in this study were malnourished women. The chi-square test revealed that fourteen variables were substantially linked with malnutrition in women. Multilayer Perceptron had the highest accuracy of 0.71 for training data but it showed poor performance for the test data set. In terms of efficiency metrics such as accuracy, kappa, and F1 scores, Random Forest outperformed the others. In comparison to the other ML algorithms tested in this study, the Random Forest technique was a significantly effective machine learning-based technique for predicting women's malnutrition in Bangladesh. The proposed approach can help identify high-risk women for malnutrition, reducing the burden on the healthcare system.},
 year = {2025}
}

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TY - JOUR
T1 - Analyze the Determinants of Malnutrition in Women and Prognosticate Nutritional Status: Insights from the Bangladesh Demographic Health Survey 2022
AU - Md. Habibur Rahman
AU - Estiyak Ahmed Turjo
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PY - 2025
N1 - https://doi.org/10.11648/j.wjph.20251001.16
DO - 10.11648/j.wjph.20251001.16
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JF - World Journal of Public Health
JO - World Journal of Public Health
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PB - Science Publishing Group
SN - 2637-6059
UR - https://doi.org/10.11648/j.wjph.20251001.16
AB - Malnutrition in women is a significant public health concern and it is a serious issue in Bangladesh. The Bangladesh Demographic Health Survey (BDHS) 2022 was utilized to identify risk variables for malnourished females and fit several machine learning-based approaches to assess their nutritional status. This study included 7972 female individuals of various locations and ages. A chi-square test with a 5% significance level was used to identify possible risk variables for malnutrition in women. Naive Bayes, CART, Logistic Regression, Random Forest, Support Vector Machine, AdaBoost, Extreme Gradient Boosting, and Multilayer Perceptron; these eight machine learning-based classifiers were used to predict malnutrition in women. Summary information revealed that 48.4% of the population analyzed in this study were malnourished women. The chi-square test revealed that fourteen variables were substantially linked with malnutrition in women. Multilayer Perceptron had the highest accuracy of 0.71 for training data but it showed poor performance for the test data set. In terms of efficiency metrics such as accuracy, kappa, and F1 scores, Random Forest outperformed the others. In comparison to the other ML algorithms tested in this study, the Random Forest technique was a significantly effective machine learning-based technique for predicting women's malnutrition in Bangladesh. The proposed approach can help identify high-risk women for malnutrition, reducing the burden on the healthcare system.
VL - 10
IS - 1
ER -

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Md. Habibur Rahman

Department of Statistics and Data Science, Jahangirnagar University, Dhaka, Bangladesh

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http://orcid.org/0000-0002-3972-3711
Estiyak Ahmed Turjo

Department of Statistics and Data Science, Jahangirnagar University, Dhaka, Bangladesh

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http://orcid.org/0009-0005-0104-6830

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APA Style

Rahman, M. H., Turjo, E. A. (2025). Analyze the Determinants of Malnutrition in Women and Prognosticate Nutritional Status: Insights from the Bangladesh Demographic Health Survey 2022. World Journal of Public Health, 10(1), 40-60. https://doi.org/10.11648/j.wjph.20251001.16

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ACS Style

Rahman, M. H.; Turjo, E. A. Analyze the Determinants of Malnutrition in Women and Prognosticate Nutritional Status: Insights from the Bangladesh Demographic Health Survey 2022. World J. Public Health 2025, 10(1), 40-60. doi: 10.11648/j.wjph.20251001.16

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AMA Style

Rahman MH, Turjo EA. Analyze the Determinants of Malnutrition in Women and Prognosticate Nutritional Status: Insights from the Bangladesh Demographic Health Survey 2022. World J Public Health. 2025;10(1):40-60. doi: 10.11648/j.wjph.20251001.16

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@article{10.11648/j.wjph.20251001.16,
  author = {Md. Habibur Rahman and Estiyak Ahmed Turjo},
  title = {Analyze the Determinants of Malnutrition in Women and Prognosticate Nutritional Status: Insights from the Bangladesh Demographic Health Survey 2022},
  journal = {World Journal of Public Health},
  volume = {10},
  number = {1},
  pages = {40-60},
  doi = {10.11648/j.wjph.20251001.16},
  url = {https://doi.org/10.11648/j.wjph.20251001.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.wjph.20251001.16},
  abstract = {Malnutrition in women is a significant public health concern and it is a serious issue in Bangladesh. The Bangladesh Demographic Health Survey (BDHS) 2022 was utilized to identify risk variables for malnourished females and fit several machine learning-based approaches to assess their nutritional status. This study included 7972 female individuals of various locations and ages. A chi-square test with a 5% significance level was used to identify possible risk variables for malnutrition in women. Naive Bayes, CART, Logistic Regression, Random Forest, Support Vector Machine, AdaBoost, Extreme Gradient Boosting, and Multilayer Perceptron; these eight machine learning-based classifiers were used to predict malnutrition in women. Summary information revealed that 48.4% of the population analyzed in this study were malnourished women. The chi-square test revealed that fourteen variables were substantially linked with malnutrition in women. Multilayer Perceptron had the highest accuracy of 0.71 for training data but it showed poor performance for the test data set. In terms of efficiency metrics such as accuracy, kappa, and F1 scores, Random Forest outperformed the others. In comparison to the other ML algorithms tested in this study, the Random Forest technique was a significantly effective machine learning-based technique for predicting women's malnutrition in Bangladesh. The proposed approach can help identify high-risk women for malnutrition, reducing the burden on the healthcare system.},
 year = {2025}
}

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