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首页> 外文会议>IEEE International Conference on Tools with Artificial Intelligence >Identifying Discriminative Attributes to Gain Insights Regarding Child Obesity in Hispanic Preschoolers Using Machine Learning Techniques
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Identifying Discriminative Attributes to Gain Insights Regarding Child Obesity in Hispanic Preschoolers Using Machine Learning Techniques

机译：识别判别性属性，以获取有关使用机器学习技术的西班牙裔学龄前儿童肥胖症的见解

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摘要

Childhood obesity is a significant problem in the United States, which affects millions of children and adolescents. Children who are obese have been found to be at greater risk for developing obesity-related health problems, such as cardiovascular disease, type 2 diabetes, and cancer later in life. Particularly, Hispanic preschoolers aged 2 to 5 years old have the highest overweight or obesity prevalence among all reported races and ethnic groups. Unfortunately, few research studies are available to identify the root cause of such a high obesity prevalence in this ethnic group. To address this issue, we recruited 238 Hispanic mothers of preschoolers to diagnose the social and epidemiological family conditions associated with barriers that challenge healthy eating. Both qualitative (focus groups, interviews) and quantitative (surveys) methods were used to assess participants behaviors. Based on the collected data, which is a large set of environmental, dietary, and feeding practices data, we utilized a well-known machine learning technique, C4.5 decision tree, to determine which variables might be important to gain insights about childhood obesity in Hispanic preschoolers. Machine learning techniques are particularly amenable to this study because they can reveal the relationship between variables as well as how each variable is related to child obesity.
机译：在美国，儿童肥胖是一个严重的问题，它影响着数以百万计的儿童和青少年。已发现肥胖儿童患肥胖相关健康问题的风险更大，例如心血管疾病，2型糖尿病和生命后期的癌症。特别是，在所有报告的种族和族裔中，年龄在2至5岁之间的西班牙裔学龄前儿童的超重或肥胖患病率最高。不幸的是，很少有研究能够确定这种种族中如此高的肥胖患病率的根本原因。为了解决这个问题，我们招募了238名学龄前儿童的西班牙裔母亲，以诊断与挑战健康饮食的障碍相关的社会和流行病学家庭状况。定性（焦点小组，访谈）和定量（调查）方法都用于评估参与者的行为。基于收集到的数据（其中包括大量的环境，饮食和喂养实践数据），我们利用了众所周知的机器学习技术C4.5决策树，以确定哪些变量可能对获取有关儿童肥胖的见解很重要在西班牙的学龄前儿童中。机器学习技术尤其适合这项研究，因为它们可以揭示变量之间的关系以及每个变量与儿童肥胖的关系。

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