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

Abstract

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.

A cross-sectional study evaluating the prevalence and predictors of malnutrition among children and adolescents visiting an urban academic hospital in Nepal

“…were transformed into zero if negative or one if positive. Principal component analysis was then performed to obtain the relative contribution of each asset in the model (11,19) . The asset with the largest contribution in differentiating the variance in households’ wealth was considered the first component (PC1).…”

Analisis Perbandingan Kinerja Model Machine Learning untuk Memprediksi Risiko Stunting pada Pertumbuhan Anak

“…Implementasi ML telah banyak di terapkan untuk melakukan prediksi dalam berbagai kasus [13], [14]. Pada kasus stunting, beberapa metode ML yang telah digunaakan adalah metode DT [15], Naïve Bayes [16], [17], SVM [18], k-Nearest Neighbors [19], Neural Networks [20], [21], Random Forest [22], [23] dan Logistic Regression [24] [25]. Selain pada kasus stunting, ML juga banyak di terapkan pada bidang Kesehatan untuk memprediksi penyakit jantung, seperti Random forest [26], Logistic Regression [27], Naïve Bayes [28], SVM [29], dan Neural Networks [29], Penyakit diabetes : Random Forest [30], Neural Network [31], Penyakit MALCOM -Vol.…”