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. 2024 May 31;19(5):e0304389.
doi: 10.1371/journal.pone.0304389. eCollection 2024.

Machine learning prediction of nutritional status among pregnant women in Bangladesh: Evidence from Bangladesh demographic and health survey 2017-18

Najma Begum  1 , Mohd Muzibur Rahman  2 , Mohammad Omar Faruk  1
Affiliations

Machine learning prediction of nutritional status among pregnant women in Bangladesh: Evidence from Bangladesh demographic and health survey 2017-18

Najma Begum et al. PLoS One. .

Abstract

Aim: Malnutrition in pregnant women significantly affects both mother and child health. This research aims to identify the best machine learning (ML) techniques for predicting the nutritional status of pregnant women in Bangladesh and detect the most essential features based on the best-performed algorithm.

Methods: This study used retrospective cross-sectional data from the Bangladeshi Demographic and Health Survey 2017-18. Different feature transformations and machine learning classifiers were applied to find the best transformation and classification model.

Results: This investigation found that robust scaling outperformed all feature transformation methods. The result shows that the Random Forest algorithm with robust scaling outperforms all other machine learning algorithms with 74.75% accuracy, 57.91% kappa statistics, 73.36% precision, 73.08% recall, and 73.09% f1 score. In addition, the Random Forest algorithm had the highest precision (76.76%) and f1 score (71.71%) for predicting the underweight class, as well as an expected precision of 82.01% and f1 score of 83.78% for the overweight/obese class when compared to other algorithms with a robust scaling method. The respondent's age, wealth index, region, husband's education level, husband's age, and occupation were crucial features for predicting the nutritional status of pregnant women in Bangladesh.

Conclusion: The proposed classifier could help predict the expected outcome and reduce the burden of malnutrition among pregnant women in Bangladesh.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. AUC (%) of different algorithms for underweight and overweight/obese classes.
Fig 2
Fig 2. Precision (%) of different algorithms for underweight and overweight/obese.
Fig 3
Fig 3. f1 score (%) of different algorithms for underweight and overweight/obese.
Fig 4
Fig 4. Recall (%) of different algorithms for underweight and overweight/obese.
Fig 5
Fig 5. Variable importance from the best model (Random Forest).
See this image and copyright information in PMC

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