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.
Highlights
In the contemporary global community, obesity is considered among the most prevalent dietary issues in both developed and developing nations
This paper presents a review of the artificial intelligence (AI) and data-driven approaches for preventing and predicting childhood obesity
It proposes conceptual frameworks that aim to use a type-2 fuzzy logic methodology, which can predict the risk of obesity for children on their family’s dirty habits patterns, characteristics, and other parameters
Summary
In the contemporary global community, obesity is considered among the most prevalent dietary issues in both developed and developing nations. It proposes conceptual frameworks that aim to use a type-2 fuzzy logic methodology, which can predict the risk of obesity for children on their family’s dirty habits patterns, characteristics, and other parameters This prediction will be used as an intervention factor to remediate obesity, which will enhance public health and reduce the costs of later treatments for several obesityrelated diseases. This paper is divided into five sections: section 2 reviews the AI and data-driven approaches used for childhood obesity; section 3 describes the conceptual framework based on type-2 fuzzy logic systems; section 4 explains the primary setup of experimental design and methodology; and section 5 comprises the conclusion and recommendations for future works
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