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Investigate the risk factors of stunting, wasting, and underweight among under-five Bangladeshi children and its prediction based on machine learning approach
- Source :
- PLoS ONE, Vol 16, Iss 6, p e0253172 (2021), PLoS ONE
- Publication Year :
- 2021
- Publisher :
- Public Library of Science (PLoS), 2021.
-
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.
- Subjects :
- Male
0301 basic medicine
Calorie
Epidemiology
Overweight
Logistic regression
computer.software_genre
Machine Learning
Geographical Locations
Families
0302 clinical medicine
Risk Factors
Medicine and Health Sciences
Prevalence
030212 general & internal medicine
Children
Wasting
Growth Disorders
Bangladesh
Multidisciplinary
Under-five
Applied Mathematics
Simulation and Modeling
Age Factors
Child, Preschool
Physical Sciences
Medicine
Female
Underweight
medicine.symptom
Algorithms
Research Article
Computer and Information Sciences
Asia
Science
Research and Analysis Methods
Machine learning
Machine Learning Algorithms
03 medical and health sciences
Thinness
Artificial Intelligence
Support Vector Machines
medicine
Humans
Artificial Neural Networks
Nutrition
Computational Neuroscience
030109 nutrition & dietetics
Wasting Syndrome
business.industry
Malnutrition
Biology and Life Sciences
Computational Biology
Infant
medicine.disease
Diet
Socioeconomic Factors
Age Groups
Medical Risk Factors
People and Places
Health survey
Population Groupings
Artificial intelligence
business
computer
Mathematics
Neuroscience
Subjects
Details
- ISSN :
- 19326203
- Volume :
- 16
- Database :
- OpenAIRE
- Journal :
- PLOS ONE
- Accession number :
- edsair.doi.dedup.....83c0041bcbe23d26b9548bf1a01c5265