来自传感器的肥胖相关数据的丰富来源,智能手机应用程序,电子医疗健康记录和保险数据可以为理解带来新的见解,预防和治疗肥胖。对于如此大的数据集,机器学习提供了复杂而优雅的工具来描述,对肥胖相关风险和结局进行分类和预测。这里,我们回顾了预测和/或分类的机器学习方法,如线性和逻辑回归,人工神经网络,深度学习和决策树分析。我们还回顾了描述和表征数据的方法,如聚类分析,主成分分析,网络科学和拓扑数据分析。我们以高级概述介绍每种方法,然后是成功应用的示例。然后将算法应用于国家健康和营养检查调查,以证明方法,效用和结果。还评估了每种方法的优势和局限性。机器学习算法的总结提供了专门应用于肥胖的数据分析状态的独特概述。
Rich sources of obesity-related data arising from sensors, smartphone apps, electronic medical health records and insurance data can bring new insights for understanding, preventing and treating obesity. For such large datasets, machine learning provides sophisticated and elegant tools to describe, classify and predict obesity-related risks and outcomes. Here, we
review machine learning methods that predict and/or classify such as linear and logistic regression, artificial neural networks, deep learning and decision tree analysis. We also
review methods that describe and characterize data such as cluster analysis, principal component analysis, network science and topological data analysis. We introduce each method with a high-level overview followed by examples of successful applications. The algorithms were then applied to National Health and Nutrition Examination Survey to demonstrate methodology, utility and outcomes. The strengths and limitations of each method were also evaluated. This summary of machine learning algorithms provides a unique overview of the state of data analysis applied specifically to obesity.