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 over

SUBMITTER: Khudri MM 

PROVIDER: S-EPMC10180666 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature