Classification of Stunting in Children Using the C4.5 Algorithm

Authors

  • Muhajir Yunus Master Program of Informatics, Universitas Ahmad Dahlan, Indonesia , Indonesia
  • Muhammad Kunta Biddinika Master Program of Informatics, Universitas Ahmad Dahlan, Indonesia , Indonesia
  • Abdul Fadlil Electrical Engineering, Universitas Ahmad Dahlan, Indonesia, Indonesia

DOI:

https://doi.org/10.15575/join.v8i1.1062

Keywords:

C4.5 Algorithm, Classification, Machine Learning, Stunting

Abstract

Stunting is a disease caused by malnutrition in children, which results in slow growth. Generally, stunting is characterized by a lack of weight and height in young children. This study aims to classify stunting in children aged 0-60 months using the Decision Tree C4.5 method based on z-score calculations with a sample size of 224 records, consisting of 4 attributes and 1 label, namely Gender, Age, Weight, Height, and Nutritional Status. The results of the study obtained a C4.5 decision tree where the Age variable influenced the classification of stunting with the highest Gain Ratio of 0.185016337. Meanwhile, the evaluation of the model using the Confusion matrix resulted in the highest accuracy of 61.82% and AUC of 0.584.

References

M. de Onis et al., “Prevalence thresholds for wasting, overweight and stunting in children under 5 years.,” Public Health Nutr., vol. 22, no. 1, pp. 175–179, Jan. 2019, doi: 10.1017/S1368980018002434.

B. J. Akombi, K. E. Agho, J. J. Hall, D. Merom, T. Astell-Burt, and A. M. N. Renzaho, “Stunting and severe stunting among children under-5 years in Nigeria: A multilevel analysis,” BMC Pediatr., vol. 17, no. 1, p. 15, 2017, doi: 10.1186/s12887-016-0770-z.

K. Astarani, D. N. T. Idris, and A. R. Oktavia, “Prevention of Stunting Through Health Education in Parents of Pre-School Children,” Str. J. Ilm. Kesehat., vol. 9, no. 1, pp. 70–77, 2020, doi: 10.30994/sjik.v9i1.270.

WHO Multicentre Growth Reference Study Group, “WHO Child Growth Standards based on length/height, weight and age.,” Acta Paediatr. Suppl., vol. 450, no. SUPPL. 450, pp. 76–85, Apr. 2006, doi: 10.1111/j.1651-2227.2006.tb02378.x.

A. K. Yadav and S. T. Karki, “Short Stature in Children Visiting Endocrine Out Patient Department of Kanti Children’s Hospital, Nepal,” J. Coll. Med. Sci., vol. 17, no. 1, pp. 55–60, 2021, doi: 10.3126/jcmsn.v17i1.36053.

H. D. S. Ferreira, “Anthropometric assessment of children’s nutritional status: A new approach based on an adaptation of Waterlow’s classification,” BMC Pediatr., vol. 20, no. 1, pp. 1–11, 2020, doi: 10.1186/s12887-020-1940-6.

World Health Organization (WHO), Global Nutrition Targets 2025, vol. 122, no. 2. World Health Organization, 2014.

M. K. and J. P. Jiawei Han, Data Mining: Concepts and Techniques, Third. Elsevier, 2012. [Online]. Available: http://library.books24x7.com/toc.aspx?bkid=44712

D. J. Hand, Principles of data mining, vol. 30, no. 7. 2007. doi: 10.2165/00002018-200730070-00010.

O. N. Chilyabanyama et al., “Performance of Machine Learning Classifiers in Classifying Stunting among Under-Five Children in Zambia,” Children, vol. 9, no. 7, Jul. 2022, doi: 10.3390/children9071082.

M. M. Islam et al., “Application of machine learning based algorithm for prediction of malnutrition among women in Bangladesh,” Int. J. Cogn. Comput. Eng., vol. 3, pp. 46–57, Jun. 2022, doi: 10.1016/j.ijcce.2022.02.002.

F. H. Bitew, C. S. Sparks, and S. H. Nyarko, “Machine learning algorithms for predicting undernutrition among under-five children in Ethiopia,” Public Health Nutr., vol. 25, no. 2, pp. 269–280, Feb. 2022, doi: 10.1017/S1368980021004262.

J. R. Quinlan, Induction of Decision Trees, vol. 1, no. 1. Springer, 1986. doi: 10.1023/A:1022643204877.

L. R. Oded Maimon, Data mining and knowledge discovery handbook, vol. 48, no. 10. 2011. doi: 10.5860/choice.48-5729.

A. E. Maxwell, T. A. Warner, and F. Fang, “Implementation of machine-learning classification in remote sensing: An applied review,” Int. J. Remote Sens., vol. 39, no. 9, pp. 2784–2817, 2018, doi: 10.1080/01431161.2018.1433343.

J. R. Quinlan, Induction of Decision Trees, vol. 1, no. 1. Machine Learning, 1986. doi: 10.1023/A:1022643204877.

D. Normawati and D. P. Ismi, “K-Fold Cross Validation for Selection of Cardiovascular Disease Diagnosis Features by Applying Rule-Based Datamining,” Signal Image Process. Lett., vol. 1, no. 2, pp. 23–35, 2019, doi: 10.31763/simple.v1i2.3.

F. Gorunescu, Data mining: Concepts, models and techniques, vol. 12. Springer, 2011. doi: 10.1007/978-3-642-19721-5.

Downloads

Published

2023-06-28

Issue

Section

Article

Citation Check