Supervised models to predict the Stunting in East Aceh

Eva Darnila, Maryana Maryana, Khalid Mawardi, Marzuki Sinambela, Iwan Pahendra


Nowadays, Undernutrition is the main cause of child death in developing countries. There are many people and organizations try to mitigate or minimize case of child death. Thus, this paper aimed to has excellent method to handle undernutrition case by exploring the efficacy of machine learning (ML) approaches to predict Stunting in East Aceh administrative zones of Indonesia and to identify the most important predictors. The study employed ML techniques using retrospective cross-sectional survey data from East Aceh, a national-representative data is collected from government by using 2019 about stunting data. We explored Random forest commonly used ML algorithms. Random Forest (RF) as an extension of bagging that in addition for taking random sample of data and also uses random subset of features which mitigates over fitting. Our results showed that the considered machine learning classification algorithms by random forest can effectively predict the stunting status in East Aceh administrative zones. Persistent stunting status was found in the east part of Aceh. The identification of high-risk zones can provide more useful information and data to decision-makers for trying to reduce child undernutrition.


Stunting, Random Forest, Machine Learning

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N. Hartaty and R. Mastura, “On overview of family knowledge on fish consumption in avoiding stunting in Meuraxa Sub-District of Banda Aceh municipality,” J. Syiah Kuala Dent. Soc., vol. 6, no. 1, pp. 18–23, 2021, doi: 10.24815/jds.v6i1.21889.

F. O. Aridiyah, N. Rohmawati, and M. Ririanty, “Faktor-faktor yang Mempengaruhi Kejadian Stunting pada Anak Balita di Wilayah Pedesaan dan Perkotaan (The Factors Affecting Stunting on Toddlers in Rural and Urban Areas),” e-Jurnal Pustaka Kesehat., 2015.

R. K. Phalkey, C. Aranda-Jan, S. Marx, B. Höfle, and R. Sauerborn, “Systematic review of current efforts to quantify the impacts of climate change on undernutrition,” Proc. Natl. Acad. Sci. U. S. A., vol. 112, no. 33, pp. E4522–E4529, 2015, doi: 10.1073/pnas.1409769112.

M. T. Niles, B. F. Emery, S. Wiltshire, M. E. Brown, B. Fisher, and T. H. Ricketts, “Climate impacts associated with reduced diet diversity in children across nineteen countries,” Environ. Res. Lett., vol. 16, no. 1, 2021, doi: 10.1088/1748-9326/abd0ab.

A. R. El-Ghannam, “The global problems of child malnutrition and mortality in different world regions,” J. Heal. Soc. Policy, vol. 16, no. 4, pp. 1–26, 2003, doi: 10.1300/J045v16n04_01.

D. L. Pelletier and E. A. Frongillo, “Changes in child survival are strongly associated with changes in malnutrition in developing countries,” J. Nutr., vol. 133, no. 1, pp. 107–119, 2003, doi: 10.1093/jn/133.1.107.

F. Habyarimana, T. Zewotir, and S. Ramroop, “A proportional odds model with complex sampling design to identify key determinants of malnutrition of children under five years in Rwanda,” Mediterr. J. Soc. Sci., vol. 5, no. 23, pp. 1642–1648, 2014, doi: 10.5901/mjss.2014.v5n23p1642.

W. Rasheed and A. Jeyakumar, “Magnitude and severity of anthropometric failure among children under two years using Composite Index of Anthropometric Failure (CIAF) and WHO standards,” Int. J. Pediatr. Adolesc. Med., vol. 5, no. 1, pp. 24–27, 2018, doi: 10.1016/j.ijpam.2017.12.003.

R. J. Biellik and W. A. Orenstein, “Strengthening routine immunization through measles-rubella elimination,” Vaccine, 2018, doi: 10.1016/j.vaccine.2018.07.029.

B. A. Goldstein, A. M. Navar, and R. E. Carter, “Moving beyond regression techniques in cardiovascular risk prediction: Applying machine learning to address analytic challenges,” Eur. Heart J., vol. 38, no. 23, pp. 1805–1814, 2017, doi: 10.1093/eurheartj/ehw302.

B. Ambale-Venkatesh et al., “Cardiovascular Event Prediction by Machine Learning: The Multi-Ethnic Study of Atherosclerosis,” Circ. Res., vol. 121, no. 9, pp. 1092–1101, 2017, doi: 10.1161/CIRCRESAHA.117.311312.

J. R. Khan, J. H. Tomal, and E. Raheem, “Model and variable selection using machine learning methods with applications to childhood stunting in Bangladesh,” Informatics Heal. Soc. Care, vol. 00, no. 00, pp. 1–18, 2021, doi: 10.1080/17538157.2021.1904938.

S. Hanieh et al., “The Stunting Tool for Early Prevention: Development and external validation of a novel tool to predict risk of stunting in children at 3 years of age,” BMJ Glob. Heal., vol. 4, no. 6, pp. 1–12, 2019, doi: 10.1136/bmjgh-2019-001801.

R. Kusumaningrum, T. A. Indihatmoko, S. R. Juwita, A. F. Hanifah, K. Khadijah, and B. Surarso, “Benchmarking of multi-class algorithms for classifying documents related to stunting,” Appl. Sci., vol. 10, no. 23, pp. 1–13, 2020, doi: 10.3390/app10238621.

A. Hadi, “The Internalization of Local Wisdom Value in Dayah Educational Institution,” J. Ilm. Peuradeun, 2017, doi: 10.26811/peuradeun.v5i2.128.

D. Ruppert, “The Elements of Statistical Learning: Data Mining, Inference, and Prediction,” J. Am. Stat. Assoc., 2004, doi: 10.1198/jasa.2004.s339.


F. ; D. Q. N. David, “School of Mathematics,” Anal. Crit. Think., vol. 15, no. 4, pp. 12–14, 2009.

E. Harrison et al., “Machine learning model demonstrates stunting at birth and systemic inflammatory biomarkers as predictors of subsequent infant growth – a four-year prospective study,” BMC Pediatr., vol. 20, no. 1, pp. 1–10, 2020, doi: 10.1186/s12887-020-02392-3.

J. W. Puspita, S. Gunadharma, S. W. Indratno, and E. Soewono, “Bayesian approach to identify spike and sharp waves in EEG data of epilepsy patients,” Biomed. Signal Process. Control, vol. 35, 2017, doi: 10.1016/j.bspc.2017.02.016.



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