Conceptual Framework Based On Type-2 Fuzzy Logic Theory for Predicting Childhood Obesity Risk

Authors

  • Khalid Almohammadi Computer Science department, Community College, University of Tabuk, Tabuk, Saudi Arabia

DOI:

https://doi.org/10.3991/ijoe.v16i03.12701

Keywords:

childhood obesity, type-2 fuzzy logic, data-driven approach

Abstract


Obesity is a critical public health concern affecting a wide range of people globally. The rise in obesity is limited to not only the wealthiest countries but also the poorest. Childhood obesity has grown exponentially in the last few years, and its progression is significant contribution to the increase in mortality rates. Childhood obesity is linked with a wide range of risk factors. These include individual and parental biological factors, sedentary behavior or decreased physical activity, and parent restriction. This paper focuses on reviewing the techniques of artificial intelligence (AI) utilized in the management of obesity in children. The paper will also propose a conceptual framework to use novel type-1 and type-2 fuzzy logic methods capable of predicting risks for developing childhood obesity. The proposed approach will address factors such as family characteristics, unhealthy food choices and lack of exercise, and others related to children and their home environment. The procedure will help in the prevention of childhood obesity, promote public health, and reduce treatment costs for a wide range of obesity-related conditions. The paper will also plan an examination of type-1 and type-2 fuzzy logic systems on approximately one thousand families in Saudi Arabia. The proposed methods can handle the encountered uncertainties to enhance modeling and promote the accuracy of predictions of the risk for childhood obesity. Type-1 and type-2 fuzzy logic systems can also encode extracted rules comprehensively to provide insight into the best childhood obesity prevention behaviors.

Author Biography

Khalid Almohammadi, Computer Science department, Community College, University of Tabuk, Tabuk, Saudi Arabia

Khalid Almohammadi completed his bachelor’s degree in computer science from Taibah University in the Kingdom of Saudi Arabia. He then pursued a profession in teaching at Tabuk University. Building on this experience, he was awarded a scholarship to complete his master’s in computer science (MSc) at Newcastle University in England, which he obtained with distinction in 2011. He recently completed his PhD in computer science from Essex University in England, which enables him to incorporate all his previous knowledge and experience in education and e-learning. The focus of his research is the development of theoretical and practical environments to enhance students’ learning experience and engagement by using fuzzy logic systems. He currently works as an assistant professor in the Computer Science Department at Community college Tabuk University.

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Published

2020-03-19

How to Cite

Almohammadi, K. (2020). Conceptual Framework Based On Type-2 Fuzzy Logic Theory for Predicting Childhood Obesity Risk. International Journal of Online and Biomedical Engineering (iJOE), 16(03), pp. 95–106. https://doi.org/10.3991/ijoe.v16i03.12701

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Section

Papers