Assessing Obesity Risk in Student: A Fuzzy Logic Approach for Precision Health
- 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.
Cite this article
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 -