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Original Articles
- Prediction of Stunting Among Under-5 Children in Rwanda Using Machine Learning Techniques
- Similien Ndagijimana, Ignace Habimana Kabano, Emmanuel Masabo, Jean Marie Ntaganda
- J Prev Med Public Health. 2023;56(1):41-49. Published online January 6, 2023
- DOI: https://doi.org/10.3961/jpmph.22.388
- 5,057 View
- 354 Download
- 3 Web of Science
- 5 Crossref
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Abstract
PDF - Objectives
Rwanda reported a stunting rate of 33% in 2020, decreasing from 38% in 2015; however, stunting remains an issue. Globally, child deaths from malnutrition stand at 45%. The best options for the early detection and treatment of stunting should be made a community policy priority, and health services remain an issue. Hence, this research aimed to develop a model for predicting stunting in Rwandan children.
Methods
The Rwanda Demographic and Health Survey 2019-2020 was used as secondary data. Stratified 10-fold cross-validation was used, and different machine learning classifiers were trained to predict stunting status. The prediction models were compared using different metrics, and the best model was chosen.
Results
The best model was developed with the gradient boosting classifier algorithm, with a training accuracy of 80.49% based on the performance indicators of several models. Based on a confusion matrix, the test accuracy, sensitivity, specificity, and F1 were calculated, yielding the model’s ability to classify stunting cases correctly at 79.33%, identify stunted children accurately at 72.51%, and categorize non-stunted children correctly at 94.49%, with an area under the curve of 0.89. The model found that the mother’s height, television, the child’s age, province, mother’s education, birth weight, and childbirth size were the most important predictors of stunting status.
Conclusions
Therefore, machine-learning techniques may be used in Rwanda to construct an accurate model that can detect the early stages of stunting and offer the best predictive attributes to help prevent and control stunting in under five Rwandan children. -
Summary
Citations
Citations to this article as recorded by
- Predicting stunting in Rwanda using artificial neural networks: a demographic health survey 2020 analysis
Similien NDAGIJIMANA, Ignace KABANO, Emmanuel MASABO, Jean Marie NTAGANDA
F1000Research.2024; 13: 128. CrossRef - Development of a diagnostic predictive model for determining child stunting in Malawi: a comparative analysis of variable selection approaches
Jonathan Mkungudza, Halima S. Twabi, Samuel O. M. Manda
BMC Medical Research Methodology.2024;[Epub] CrossRef - Predicting harmful alcohol use prevalence in Sub-Saharan Africa between 2015 and 2019: Evidence from population-based HIV impact assessment
Mtumbi Goma, Wingston Felix Ng’ambi, Cosmas Zyambo, Yimam Getaneh Misganie
PLOS ONE.2024; 19(10): e0301735. CrossRef - Hybrid Machine Learning for Stunting Prevalence: A Novel Comprehensive Approach to Its Classification, Prediction, and Clustering Optimization in Aceh, Indonesia
Novia Hasdyna, Rozzi Kesuma Dinata, Rahmi, T. Irfan Fajri
Informatics.2024; 11(4): 89. CrossRef - Child stunting prevalence determination at sector level in Rwanda using small area estimation
Innocent Ngaruye, Joseph Nzabanita, François Niragire, Theogene Rizinde, Joseph Nkurunziza, Jean Bosco Ndikubwimana, Charles Ruranga, Ignace Kabano, Dieudonne N. Muhoza, Jeanine Ahishakiye
BMC Nutrition.2023;[Epub] CrossRef
- The Effect of the Physical Factors of Parents and Children on Stunting at Birth Among Newborns in Indonesia
- Kencana Sari, Ratu Ayu Dewi Sartika
- J Prev Med Public Health. 2021;54(5):309-316. Published online August 29, 2021
- DOI: https://doi.org/10.3961/jpmph.21.120
- 6,765 View
- 534 Download
- 8 Web of Science
- 15 Crossref
-
Abstract
PDF
- Objectives
This study examined stunting at birth and its associations with physical factors of parents and children in Indonesia.
Methods
This study analyzed secondary data from the national cross-sectional Indonesian Basic Health Survey 2018, conducted across 34 provinces and 514 districts/cities. Birth length data were available for 756 newborns. Univariable, bivariable, and multivariable logistic regression analyses were performed to determine associations between the physical factors of parents and children and stunting at birth.
Results
In total, 10.2% of children aged 0 months were stunted at birth (10.7% of males and 9.5% of females). Stunting at birth was associated with the mother’s age at first pregnancy, parity, parents’ heights, parents’ ages, and gestational age. Children from mothers with short statures (height <145.0 cm) and fathers with short statures (height <161.9 cm) had an almost 6 times higher likelihood of being stunted at birth (adjusted odds ratio, 5.93; 95% confidence interval, 5.53 to 6.36). A higher maternal age at first pregnancy had a protective effect against stunting. However, other variables (firstborn child, preterm birth, and both parents’ ages being <20 or >35 years) corresponded to a 2-fold higher likelihood of stunting at birth compared to the reference.
Conclusions
These findings provide evidence that interventions to reduce stunting aimed at pregnant females should also consider the parents’ stature, age, and parity, particularly if it is the first pregnancy and if the parents are short in stature or young. Robust programs to support pregnant females and monitor children’s heights from birth will help prevent intergenerational stunting. -
Summary
Citations
Citations to this article as recorded by- How do household living conditions and gender-related decision-making influence child stunting in Rwanda? A population-based study
Jean Nepo Utumatwishima, Ingrid Mogren, Aline Umubyeyi, Ali Mansourian, Gunilla Krantz, Olutosin Ademola Otekunrin
PLOS ONE.2024; 19(3): e0290919. CrossRef - Understanding Pediatric Health Trends in Papua: Insights From SUSENAS, RISKESDAS, Remote Sensing, and Its Relevance to Prabowo and Gibran’s Free Lunch and Milk Program
Rezzy Eko Caraka, Khairunnisa Supardi, Puspita Anggraini Kaban, Robert Kurniawan, Prana Ugiana Gio, Yunho Kim, Syihabuddin Ahmad Mufti, Rung-Ching Chen, Muhammad Khahfi Zuhanda, Avia Enggar Tyasti, Noor Ell Goldameir, Bens Pardamean
IEEE Access.2024; 12: 51536. CrossRef - Stunting predictors among children aged 0-24 months in Southeast Asia: a scoping review
Via Eliadora Togatorop, Laili Rahayuwati, Raini Diah Susanti, Julianus Yudhistira Tan
Revista Brasileira de Enfermagem.2024;[Epub] CrossRef - Children’s sex composition and modern contraceptive use among mothers in Bangladesh
Md. Nuruzzaman Khan, Shimlin Jahan Khanam, Md Arif Billah, Md Mostaured Ali Khan, M Mofizul Islam, José Antonio Ortega
PLOS ONE.2024; 19(5): e0297658. CrossRef - Peran Ayah terhadap Kejadian Stunting pada Balita di Perdesaan
Elya Sugianti, Berliana Devianti Putri, Annas Buanasita
Amerta Nutrition.2024; 8(2): 214. CrossRef - Risk factors associated with stunting incidence in under five children in Southeast Asia: a scoping review
Devi Azriani, Masita, Nabila Salma Qinthara, Intan Nurma Yulita, Dwi Agustian, Yenni Zuhairini, Meita Dhamayanti
Journal of Health, Population and Nutrition.2024;[Epub] CrossRef - Stunting at birth: linear growth failure at an early age among newborns in Hawassa city public health hospitals, Sidama region, Ethiopia: a facility-based cross-sectional study
Haileyesus Ejigu, Zelalem Tafese
Journal of Nutritional Science.2023;[Epub] CrossRef - Socio-economic and agricultural factors associated with stunting of under 5-year children: findings from surveys in mountains, dry zone and delta regions of rural Myanmar (2016–2017)
Min Kyaw Htet, Tran Thanh Do, Thet Wah, Thant Zin, Myat Pan Hmone, Shahreen Raihana, Elizabeth Kirkwood, Lwin Mar Hlaing, Michael J Dibley
Public Health Nutrition.2023; 26(8): 1644. CrossRef - Predictor of Stunting Among Children 0-24 Months Old in Indonesia: A Scoping Review
Via Eliadora Togatorop, Laili Rahayuwati, Raini Diah Susanti
Jurnal Obsesi : Jurnal Pendidikan Anak Usia Dini.2023; 7(5): 5654. CrossRef - SOCIALIZING ADOLESCENT REPORDUCTIVE HEALTH: EFFORTS TO PREVENT EARLY MARRIAGE AND REDUCE UNINTENDED PREGNANCIES AMONG ADOLESCENTS
Ilham Rahmanto, Husnul Khatimah, Laila Hidayah Santoso, Nabilah Apsari Devitri, Airinda Gustika Ningrum, Allfatiana Suci Andriani, Arbiyan Syayid Nurdin, Enina Patricia, Hanif Nur Setyawan, Syahrani Meutia Tifanny
Jurnal Layanan Masyarakat (Journal of Public Services).2023; 7(3): 375. CrossRef - EARLY DETECTION FOR CHILD GROWTH AND DEVELOPMENT IN POSYANDU DADAPKUNING VILLAGE, CERME-GRESIK SUB-DISTRICT
Mira Triharini, Monica Octa Alfiana, Naurah Syafiqah Larasati , Sharfina Az-Zahrin Hakim , Puti Hanalya Rengganis
Jurnal Pengabdian Masyarakat Dalam Kesehatan.2023; 5(2): 53. CrossRef - Faktor Determinan Panjang Badan Bayi Lahir Pendek sebagai Faktor Risiko Stunting di Jawa Barat
Judiono Judiono, Witri Priawantiputri, Noormarina Indraswari, Mutiara Widawati, Mara Ipa, Ginna Megawati, Heni Prasetyowati, Dewi Marhaeni
Amerta Nutrition.2023; 7(2): 240. CrossRef - Risk Factors Related to Stunting
Tri Anugrah Oktaviani, Linda Suwarni, Selviana Selviana
JURNAL INFO KESEHATAN.2023; 21(4): 854. CrossRef - Determinants of Incident Stunting in Elementary School Children in Endemic Area Iodine Deficiency Disorders Enrekang Regency
Nur Abri, Saifuddin Sirajuddin, Burhanuddin Bahar, Nurhaedar Jafar, Syamsiar S. Russeng, Zakaria Zakaria, Veni Hadju, Abdul Salam, Abdul Razak Thaha
Open Access Macedonian Journal of Medical Sciences.2022; 10(E): 161. CrossRef - Faktor Berkaitan dengan Stunting dan Wasting pada Pasien Onkologi Anak
Maya Utami Widhianti, Listiyani Eka Tyastuti, Meika Rahmawati Arifah, Karima Rizqi Alviani, Hagnyonowati
Amerta Nutrition.2022; 6(1SP): 133. CrossRef
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