Mulyono, TM, Natalia, F and Sudirman, S (2020) A Study of Data Mining Methods for Identification Undernutrition and Overnutrition in Obesity. In: ICSEB '19: Proceedings of the 2019 3rd International Conference on Software and e-Business . pp. 6-10. (3rd International Conference on Software and e e-Business, 09 December 2019 - 12 December 2019, Tokyo).

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Abstract

Indonesia is currently experiencing the Double Burden Nutrition problem. This is a problem when a significant proportion of the population is endangerd with the problem of malnutrition, but a significant other suffer from obesity or over-nutrition. People who are underweight, overweight, and obesity are included in the ten risks in terms of the global disease burden. In 2015 showing that the adult population in the world is overweight, with 38% of men and 40% of women. This study aims to detect over nutrition, undernutrition in obese sufferers to determine their nutritional status with 24 hours food recall method for assessing their consumption and using data mining algorithms, namely: K-Nearest Neighbor, Naïve Bayesian Classification, and Decision Tree for data mining calculations. From the results obtained with the help of RapidMiner tools, it can be concluded that not all obese sufferers are over nutritioned, they can also experience undernutrition or normal nutrition. From the results of this study, the best accuracy for calculating nutritional status is using the Naïve Bayesian Classification algorithm with an accuracy rate of 100 %, then the Decision Tree with an accuracy rate of 86.67% and the last is K-Nearest Neighbor with an accuracy rate of 73.33%.

Item Type:	Conference or Workshop Item (Paper)
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine
Divisions:	Computer Science & Mathematics
Publisher:	Association for Computing Machinery
Date Deposited:	05 Dec 2019 12:21
Last Modified:	12 Jun 2024 14:07
DOI or ID number:	10.1145/3374549.3374565
URI:	https://researchonline.ljmu.ac.uk/id/eprint/11859

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