Predicting nutritional status for women of childbearing age from their economic, health, and demographic features: A supervised machine learning approach.

Autor: Khudri MM; Department of Economics, Fogelman College of Business and Economics, The University of Memphis, Memphis, Tennessee, United States of America., Rhee KK; Department of Economics, Fogelman College of Business and Economics, The University of Memphis, Memphis, Tennessee, United States of America., Hasan MS; Department of Computer Science, Virginia Tech, Blacksburg, VA, United States of America., Ahsan KZ; Public Health Leadership Program, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2023 May 12; Vol. 18 (5), pp. e0277738. Date of Electronic Publication: 2023 May 12 (Print Publication: 2023).
DOI: 10.1371/journal.pone.0277738
Abstrakt: Background: Malnutrition imposes enormous costs resulting from lost investments in human capital and increased healthcare expenditures. There is a dearth of research focusing on the prediction of women's body mass index (BMI) and malnutrition outcomes (underweight, overweight, and obesity) in developing countries. This paper attempts to fill out this knowledge gap by predicting the BMI and the risks of malnutrition outcomes for Bangladeshi women of childbearing age from their economic, health, and demographic features.
Methods: Data from the 2017-18 Bangladesh Demographic and Health Survey and a series of supervised machine learning (SML) techniques are used. Additionally, this study circumvents the imbalanced distribution problem in obesity classification by utilizing an oversampling approach.
Results: Study findings demonstrate that the support vector machine and k-nearest neighbor are the two best-performing methods in BMI prediction based on the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). The combined predictor algorithms consistently yield top specificity, Cohen's kappa, F1-score, and AUC in classifying the malnutrition status, and their performance is robust to alternative standards. The feature importance ranking based on several nonparametric and combined predictors indicates that socioeconomic status, women's age, and breastfeeding status are the most important features in predicting women's nutritional outcomes. Furthermore, the conditional inference trees corroborate that those three features, along with the partner's educational attainment and employment status, significantly predict malnutrition risks.
Conclusion: To the best of our knowledge, this is the first study that predicts BMI and one of the pioneer studies to classify all three malnutrition outcomes for women of childbearing age in Bangladesh, let alone in any lower-middle income country, using SML techniques. Moreover, in the context of Bangladesh, this paper is the first to identify and rank features that are critical in predicting nutritional outcomes using several feature selection algorithms. The estimators from this study predict the outcomes of interest most accurately and efficiently compared to other existing studies in the relevant literature. Therefore, study findings can aid policymakers in designing policy and programmatic approaches to address the double burden of malnutrition among Bangladeshi women, thereby reducing the country's economic burden.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2023 Khudri et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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