Proceedings of the International Conference on Applied Science and Technology on Engineering Science 2023 (iCAST-ES 2023)

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Assessing Obesity Risk in Student: A Fuzzy Logic Approach for Precision Health

Authors
Nurohmat1, *, Nur Budi Nugraha1
1Indramayu State Polytechnic, Indramayu, West Java, Indonesia
*Corresponding author.
Corresponding Author
Nurohmat
Available Online 17 February 2024.
DOI
10.2991/978-94-6463-364-1_62How to use a DOI?
Keywords
Body Mass Index (BMI); Fuzzy Logic; Health; Obesity
Abstract

Obesity is a significant and increasing global health challenge, impacting individuals and society on multiple fronts. Its prevalence continues to increase and attacks individuals from various age groups, including students at the Polytechnic. The transition from adolescence to adulthood, a period marked by the attainment of higher education, is a critical period in the formation of health behaviors and lifestyle habits. This phase offers an important opportunity to identify and reduce risk factors associated with obesity. Conventional methods for assessing obesity risk, such as Body Mass Index (BMI), although useful, often oversimplify the complex web of factors that contribute to obesity. These methods fail to account for the complex interactions between genetic, environmental, and lifestyle variables, all of which play an important role in the development of obesity. This research applies innovative fuzzy logic in assessing the risk of obesity among students, thereby advancing the field of precision health. Our research seeks to bridge the gap between conventional risk assessment methods and the complex reality of obesity development, by providing insight into effectively identifying and supporting students at risk of obesity. The result of research shows that the system that has been created can identify the level of risk of obesity in students. The obesity risk results for the students were obtained with the respective obesity risk Low = 0.659991319967, Medium obesity risk = 0.340008680033 and High obesity risk = 0.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Applied Science and Technology on Engineering Science 2023 (iCAST-ES 2023)
Series
Advances in Engineering Research
Publication Date
17 February 2024
ISBN
10.2991/978-94-6463-364-1_62
ISSN
2352-5401
DOI
10.2991/978-94-6463-364-1_62How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Nurohmat
AU  - Nur Budi Nugraha
PY  - 2024
DA  - 2024/02/17
TI  - Assessing Obesity Risk in Student: A Fuzzy Logic Approach for Precision Health
BT  - Proceedings of the International Conference on Applied Science and Technology on Engineering Science 2023 (iCAST-ES 2023)
PB  - Atlantis Press
SP  - 673
EP  - 686
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-364-1_62
DO  - 10.2991/978-94-6463-364-1_62
ID  - 2024
ER  -