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

机译：基于2型模糊逻辑理论预测儿童肥胖风险的概念框架

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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.
机译：肥胖是一项影响全球各种人民的关键公共卫生问题。肥胖的兴起不仅限于最富裕的国家，也是最贫穷的国家。在过去的几年里，儿童肥胖症已呈指数级增长，其进展是对死亡率增加的重大贡献。儿童肥胖与广泛的风险因素有关。这些包括个人和亲本生物因素，久坐不动行或降低的身体活动，以及父母限制。本文侧重于审查在儿童肥胖管理中使用的人工智能（AI）的技术。本文还将提出一种概念框架，用于使用能够预测发展儿童肥胖的风险的新型1型和2型模糊逻辑方法。拟议的方法将解决家庭特征，不健康的食物选择和缺乏运动等因素，以及与儿童及其家庭环境有关的其他人。该程序将有助于预防儿童肥胖，促进公共卫生，并降低各种肥胖相关条件的治疗费用。本文还将在沙特阿拉伯约一千个家庭上规划1型和2型模糊逻辑系统的检查。该方法可以处理遇到的不确定性，以提高建模，促进儿童肥胖风险预测的准确性。类型-1和2型模糊逻辑系统也可以全面编码提取的规则，以提供对最佳儿童肥胖预防行为的洞察力。

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