Department of Industrial, Manufacturing & Systems Engineering, Texas Tech University, Lubbock, TX 79409, USA.
Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409, USA.
Sensors (Basel). 2023 Jan 9;23(2):759. doi: 10.3390/s23020759.
Childhood obesity is a public health concern in the United States. Consequences of childhood obesity include metabolic disease and heart, lung, kidney, and other health-related comorbidities. Therefore, the early determination of obesity risk is needed and predicting the trend of a child's body mass index (BMI) at an early age is crucial. Early identification of obesity can lead to early prevention. Multiple methods have been tested and evaluated to assess obesity trends in children. Available growth charts help determine a child's current obesity level but do not predict future obesity risk. The present methods of predicting obesity include regression analysis and machine learning-based classifications and risk factor (threshold)-based categorizations based on specific criteria. All the present techniques, especially current machine learning-based methods, require longitudinal data and information on a large number of variables related to a child's growth (e.g., socioeconomic, family-related factors) in order to predict future obesity-risk. In this paper, we propose three different techniques for three different scenarios to predict childhood obesity based on machine learning approaches and apply them to real data. Our proposed methods predict obesity for children at five years of age using the following three data sets: (1) a single well-child visit, (2) multiple well-child visits under the age of two, and (3) multiple random well-child visits under the age of five. Our models are especially important for situations where only the current patient information is available rather than having multiple data points from regular spaced well-child visits. Our models predict obesity using basic information such as birth BMI, gestational age, BMI measures from well-child visits, and gender. Our models can predict a child's obesity category (normal, overweight, or obese) at five years of age with an accuracy of 89%, 77%, and 89%, for the three application scenarios, respectively. Therefore, our proposed models can assist healthcare professionals by acting as a decision support tool to aid in predicting childhood obesity early in order to reduce obesity-related complications, and in turn, improve healthcare.
儿童肥胖是美国的一个公共卫生关注点。儿童肥胖的后果包括代谢疾病以及心脏、肺、肾脏和其他与健康相关的合并症。因此，需要早期确定肥胖风险，并且早期预测儿童的体重指数 (BMI) 趋势至关重要。早期识别肥胖可以实现早期预防。已经测试和评估了多种方法来评估儿童肥胖趋势。现有的生长图表有助于确定儿童当前的肥胖水平，但无法预测未来的肥胖风险。预测肥胖的现有方法包括回归分析以及基于机器学习的分类和基于特定标准的风险因素（阈值）分类。所有现有的技术，尤其是当前基于机器学习的方法，都需要纵向数据和与儿童生长相关的大量变量的信息（例如，社会经济、家庭相关因素），以便预测未来的肥胖风险。在本文中，我们提出了三种基于机器学习方法的不同技术，用于根据三种不同场景预测儿童肥胖，并将其应用于真实数据。我们提出的方法使用以下三个数据集来预测五岁儿童的肥胖：（1）单次健康儿童就诊，（2）两岁以下的多次健康儿童就诊，以及（3）五岁以下的多次随机健康儿童就诊。我们的方法在仅可获得当前患者信息而不是定期间隔的健康儿童就诊的多个数据点的情况下尤其重要。我们的模型使用基本信息（如出生 BMI、胎龄、健康儿童就诊的 BMI 测量值和性别）来预测肥胖。我们的模型可以在三个应用场景中分别以 89%、77%和 89%的准确率预测儿童五岁时的肥胖类别（正常、超重或肥胖）。因此，我们提出的模型可以通过充当决策支持工具来帮助医疗保健专业人员尽早预测儿童肥胖，以减少肥胖相关并发症，进而改善医疗保健。
