Prediction of stunting and its socioeconomic determinants among adolescent girls in Ethiopia using machine learning algorithms

• Published in: PloS one Vol. 20; no. 1; p. e0316452
• Main Authors: Zemariam, Alemu Birara, Abate, Biruk Beletew, Alamaw, Addis Wondmagegn, Lake, Eyob shitie, Yilak, Gizachew, Ayele, Mulat, Tilahun, Befkad Derese, Ngusie, Habtamu Setegn
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 24.01.2025; Public Library of Science (PLoS)
• Subjects: Adolescence; Adolescent; Adolescents; Age; Algorithms; Biology and Life Sciences; Child development; Children; Children & youth; Cognitive ability; Computer and Information Sciences; Contraceptives; Data mining; Decision making; Decision support systems; Decision trees; Developing countries; Drinking water; Economic aspects; Education; Engineering and Technology; Ethiopia - epidemiology; Feature selection; Female; Girls; Growth Disorders - diagnosis; Growth Disorders - epidemiology; Health aspects; Humans; LDCs; Learning algorithms; Machine Learning; Malnutrition; Medicine and Health Sciences; Nutrition; Nutrition disorders; Nutritional Status; Nutritionally induced diseases; Observational learning; People and Places; Performance evaluation; Physical Sciences; Population; Prediction models; Public health; Python; Questionnaires; Research and Analysis Methods; Sanitation; Social aspects; Socioeconomic Factors; Software; Teenage girls; Teenagers; Toilet facilities; Undernutrition; Womens health; Young Adult; Ethiopia
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0316452

Stunting is a vital indicator of chronic undernutrition that reveals a failure to reach linear growth. Investigating growth and nutrition status during adolescence, in addition to infancy and childhood is very crucial. However, the available studies in Ethiopia have been usually focused in early childhood and they used the traditional stastical methods. Therefore, this study aimed to employ multiple machine learning algorithms to identify the most effective model for the prediction of stunting among adolescent girls in Ethiopia. A total of 3156 weighted samples of adolescent girls aged 15-19 years were used from the 2016 Ethiopian Demographic and Health Survey dataset. The data was pre-processed, and 80% and 20% of the observations were used for training, and testing the model, respectively. Eight machine learning algorithms were included for consideration of model building and comparison. The performance of the predictive model was evaluated using evaluation metrics value through Python software. The synthetic minority oversampling technique was used for data balancing and Boruta algorithm was used to identify best features. Association rule mining using an Apriori algorithm was employed to generate the best rule for the association between the independent feature and the targeted feature using R software. The random forest classifier (sensitivity = 81%, accuracy = 77%, precision = 75%, f1-score = 78%, AUC = 85%) outperformed in predicting stunting compared to other ML algorithms considered in this study. Region, poor wealth index, no formal education, unimproved toilet facility, rural residence, not used contraceptive method, religion, age, no media exposure, occupation, and having one or more children were the top attributes to predict stunting. Association rule mining was identified the top seven best rules that most frequently associated with stunting among adolescent girls in Ethiopia. The random forest classifier outperformed in predicting and identifying the relevant predictors of stunting. Results have shown that machine learning algorithms can accurately predict stunting, making them potentially valuable as decision-support tools for the relevant stakeholders and giving emphasis for the identified predictors could be an important intervention to halt stunting among adolescent girls.