Investigate the risk factors of stunting, wasting, and underweight among under-five Bangladeshi children and its prediction based on machine learning approach.
Category:- Journal; Year:- 2021
Discipline:- Statistics Discipline
School:- Science, Engineering & Technology School
Abstract
Aims
Malnutrition is a major health issue among Bangladeshi under-five (U5)
children. Children are malnourished if the calories and proteins they take
through their diet are not sufficient for their growth and maintenance. The
goal of the research was to use machine learning (ML) algorithms to detect the
risk factors of malnutrition (stunted, wasted, and underweight) as well as
their prediction. Methods This work utilized malnutrition data that was derived
from Bangladesh Demographic and Health Survey which was conducted in 2014. The
selected dataset consisted of 7079 children with 13 factors. The potential
risks of malnutrition have been identified by logistic regression (LR).
Moreover, 3 ML classifiers (support vector machine (SVM), random forest (RF),
and LR) have been implemented for predicting malnutrition and the performance
of these ML algorithms were assessed on the basis of accuracy. Results The
average prevalence of stunted, wasted, and underweight was 35.4%, 15.4%, and
32.8%, respectively. It was noted that LR identified five risk factors for
stunting and underweight, as well as four factors for wasting. Results
illustrated that RF can be accurately classified as stunted, wasted, and
underweight children and obtained the highest accuracy of 88.3% for stunted, 87.7%
for wasted, and 85.7% for underweight. Conclusion This research focused on the
identification and prediction of major risk factors for stunting, wasting, and
underweight using ML algorithms which will aid policymakers in reducing
malnutrition among Bangladesh’s U5 children.