Technological Innovation is Needed to Accelerate Stunting Reduction in Indonesia
DOI:
https://doi.org/10.26555/eshr.v4i2.6369Keywords:
Stunting, Internet of Things, Innovation, Cohort data, Artificial intelligenceAbstract
The Indonesian government has implemented programs to reduce stunting by targeting several groups, including: pregnant and maternity mothers, toddlers, school-age children, adolescents, and young adults. The actions include interventions and efforts to increase knowledge among the related subject – mostly among women. These efforts must still have been carried out until recently. However, along with the development of the digital era, stunting prevention needs to involve technology as an innovation to predict the possibility of a toddler becoming stunted in the future when their intake is insufficient.
References
World Health Organization. Malnutrition. Web. 2022 [cited 2022 Jun 5]. Available from: https://www.who.int/health-topics/malnutrition#tab=tab_1
Kementerian Kesehatan RI. Buletin Jendela Data dan Informasi Kesehatan: Situasi Balita Pendek (Stunting) di Indonesia. Kementeri Kesehat RI. 2018;20.
Kemenkes RI. Buletin Stunting. Kementeri Kesehat RI. 2018;301(5):1163–78.
Kugler L. Artificial intelligence, machine learning, and the fight against world hunger. Commun ACM. 2022 Feb;65(2):17–9. Available from: https://dl.acm.org/doi/10.1145/3503779
Yang Y, Wang H, Jiang R, Guo X, Cheng J, Chen Y. A Review of IoT-Enabled Mobile Healthcare: Technologies, Challenges, and Future Trends. IEEE Internet Things J. 2022 Jun 15;9(12):9478–502. Available from: https://ieeexplore.ieee.org/document/9686065/
Chilyabanyama ON, Chilengi R, Simuyandi M, Chisenga CC, Chirwa M, Hamusonde K, et al. Performance of Machine Learning Classifiers in Classifying Stunting among Under-Five Children in Zambia. Children. 2022 Jul 20;9(7):1082. Available from: https://www.mdpi.com/2227-9067/9/7/1082
Bitew FH, Sparks CS, Nyarko SH. Machine learning algorithms for predicting undernutrition among under-five children in Ethiopia. Public Health Nutr. 2021 Oct 8;1–12. Available from: https://www.cambridge.org/core/product/identifier/S1368980021004262/type/journal_article
Islam MM, Rahman MJ, Islam MM, Roy DC, Ahmed NAMF, Hussain S, et al. Application of machine learning based algorithm for prediction of malnutrition among women in Bangladesh. Int J Cogn Comput Eng. 2022 Jun;3:46–57. Available from: https://linkinghub.elsevier.com/retrieve/pii/S2666307422000067
Khan W, Zaki N, Masud MM, Ahmad A, Ali L, Ali N, et al. Infant birth weight estimation and low birth weight classification in United Arab Emirates using machine learning algorithms. Sci Rep. 2022 Dec 15;12(1):12110. Available from: https://www.nature.com/articles/s41598-022-14393-6
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