IJBTT

Malnutrition among Filipino Children: A Predictive Analysis using Data Mining Approach
	© 2022 by IJCTT Journal
	Volume-70 Issue-1
	Year of Publication : 2022
	Authors : Racquel L. Pula, Rosanna A. Esquivel
	DOI :  10.14445/22312803/IJCTT-V70I1P104

How to Cite?

Racquel L. Pula, Rosanna A. Esquivel, "Malnutrition among Filipino Children: A Predictive Analysis using Data Mining Approach," International Journal of Computer Trends and Technology, vol. 70, no. 1, pp. 20-24, 2022. Crossref, https://doi.org/10.14445/22312803/IJCTT-V70I1P104

Abstract
Despite being one of the fastest-growing economies, the Philippines still experiences pressing health issues such as malnutrition, which is characterized as either overweight or underweight and generally defined as a nutritional deficiency. As a result, various government agencies, in collaboration with Local Government Units (LGUs) and other partner Non-Governmental Organizations (NGOs), are working to address these nutrition-related issues. In Cabanatuan City, the City Nutrition Committee (CNC) prepared the City Nutrition Action Plan (CNAP) to improve the nutritional status of those tested and address the core causes of malnutrition. With this setting in mind, the goal of this study was to use a data mining approach to do a predictive analysis of malnutrition among children aged six (6) years and under in Cabanatuan City. The study was carried out at 19 Day Care Centers (DCC) in District IV of Cabanatuan City. The population of children designated as malnourished – either underweight or overweight – from 2016 to 2018 was used in the prediction analysis. In this work, data mining and clustering techniques were applied. The analysis showed that in the three (3) years following the period under survey, underweight cases would be decreasing while overweight cases would be increasing. This study concludes that data mining and clustering methods, with the help of predictive analysis assessment, are appropriate in predicting certain phenomena, such as malnutrition cases. However, it is recommended that further studies be conducted to determine and explore more variables that bear on the malnutrition among children in the City of Cabanatuan. Moreover, an in-depth predictive analysis of malnutrition in the area may also be conducted. The analysis results help develop policies and regulations that will help overcome the malnutrition problem in the country.

Keywords
Cabanatuan city, Data mining, K-means, Clustering, Predictive analysis.

Reference

[1] Blosser, M., Onis, M., Malnutrition: quantifying the health impact at national and local levels. World Health Organization (2005).
[2] Cabanatuan City Government, City Nutrition Plan. Technical Report. Cabanatuan City Health Office (2019).
[3] Cabanatuan City Government, Socio-Economic Profile(2019).
[4] Chakraborti S, Gupta SB, Chaturvedi B, Chakraborty SK. A study of protein-energy malnutrition (PEM) in children (0 to 6 years) in a rural population of Jhansi district (UP). Indian J Community Med. 31(4) (2006) 291-2
[5] Food and Nutrition Research Institute, 2015. National nutrition survey.
[6] Lina, Joey D., Malnutrition: a gnawing crisis needing urgent action. Retrieved from https://www.pressreader.com/ (2018).
[7] Pelletier, D. L., Frongillo, E. A., Habicht, J. P., Epidemiologic evidence for a potentiating effect of malnutrition on child mortality (1993).
[8] Philippine Statistics Authority, Volume of Palay Production in Central Luzon, July to December (2018). Retrieved from http://rsso03.psa.gov.ph/article/volume-palay-production-central-luzon-july-december-2018
[9] Shukla N, Toppo N.A., Thakur A, Kasar P.K. A study of malnutrition and associated risk factors among children of age 06-59 months in an urban area of Jabalpur district (M.P.). Int J Community Med Public Health (5) (2018) 296-301.
[10] Singh, S., S. S. Gill, Analysis and study of a k-means clustering algorithm. International Journal of Engineering Research and Technology. 2(7) (2013).
[11] UNICEF, Philippines. Poor diets, failing food systems, and lack of physical activity are causing overweight and obesity in children. Retrieved from https://www.unicef.org/philippines/press-releases/poor-diets-failing-food-systems-and-lack-physical-activity-are-causing-overweight
[12] United Nations International Children’s Emergency Fund, Malnutrition rates remain alarming: stunting is declining too slowly while wasting still impacts the lives of far too many young children (2018). Retrieved from https://data.unicef.org/topic/nutrition/malnutrition/.
[13] Winiarti, Yulianshi, Purnama, Identification of toddlers’ nutritional status using data mining approach. International Journal of Advanced Computer Science and Application, 9(1) (2018)
[14] S. M. Metevand V. P. Veiko, Laser Assisted Microtechnology, 2nd ed., R. M. Osgood, Jr., Ed. Berlin, Germany: Springer-Verlag. 5(3) (1998) 300-320.
[15] Swani, L., Tyagi, P., Predicted Analytics through Data Mining. International Journal of Engineering and Technology. 04(09) (2017) ISSN 2395-0072
[16] Vijayukumar, V., Predictive Learning Analytics for Student Success towards Outcome Based Education Perspective. International Journal of Computer Trends and Technology, 69(9) (2021) 7-11, ISSN 2231 – 2803
[17] Purnapatre, H., Shukla, S., Pareek, S., Predictive Analysis, Predictive Analysis as a Solution to Branding. International Journal of Computer Trends and Technology, 68(12) (2020) 47-63. ISSN 2231 – 2803
[18] Daniel, A. O., Uwem, E. E., Tululope, O. D., Jesupelumi, O. S., 2020. Prediction and Diagnosis of Liver Disease using Machine Learning. International Journal of Computer Trends and Technology, 68(8) (2020). ISSN 2231 – 2803
[19] Sharma, A., Sharma, N., Text Summarization using K-Means, Tanimoto Distance and Jacard Similarity. International Journal of Computer Trends and Technology, 68(6) (2020). ISSN 2231 – 2803
[20] Singh, K., Malik, D., Sharma, N., Evolving Limitations in K-Means Algorithm in Data Mining and their Removal. International Journal of Computation Engineering and Management, 12 (2011). ISSN 2230-7893
[21] Inter-Agency Regional Analysts Network, Asia Report: Socio-Economy of Chronic Malnutrition in the Philippines: A Preliminary Key Trends Analysis by 2030 (2016).
[22] Shalini, L., Goplai Naga Sravya, Analysis of Health-Tweets using K-means Clustering. International Journal of Engineering and Technology. 05(08) (2018). ISSN 2395-0072
[23] Narang, B., Verma, P., Kochar, P., Application-Based, Advantageous K-Means Clustering Algorithm in Data Mining – A Review. International Journal of Latest Trends in Engineering and Technology. 7(2) (2016). ISSN 2278-621X
[24] Ogbuabor, G., Ugwoke, F. N., Clustering Algorithm for a Healthcare Data Set using Silhouette Score Value. International Journal of Computer Science and Information Technology, 10(2) (2018).
[25] Haraty, R. A., Dimishkieh, M., Masud, M., An Enhanced k-Means Clustering Algorithm for Pattern Discovery in Healthcare Data. International Journal of Distributed Sensor Networks, 15 (2015). http://dx.doi.org/10.1155/2015/615740
[26] Kanungo, T., Netanyahu, N. S., Wu, A. Y., An Efficient k-MeNS Clustering Algorithm: Analysis and Implementation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7) (2002).