2014-16年ATUS调查中个人收入水平对BMI的影响及使用各种机器学习方法的表现分析

Neha Singh, Sinu Mathew, Neha Kunte
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引用次数: 0

摘要

身体质量指数(BMI)是一种被广泛接受的衡量一个人各种健康风险的指标。个人的饮食习惯、压力和生活方式是影响我们健康的一些因素。所有这些都导致了体重指数的增加,进而导致了与超重和肥胖有关的更多健康问题。本文利用2014年美国时间使用调查(American Time Use Survey, ATUS) Eating & Health Module Files调查中的数据,根据收入预测人们的BMI,并试图建立基于家庭收入的超重/肥胖可能性之间的关系。为了进行此分析,将使用不同的机器学习算法,最后在ROC曲线的帮助下对所有算法进行比较。
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Impact of Income Level of an Individual on his BMI and Performance Analysis using Various Machine Learning Approaches on ATUS Survey 2014–16
Body Mass Index(BMI) is widely accepted as a measure to assess various health risks of a person. Eating habits, stress and lifestyle followed by an individual are some of the factors that affect our health. All these contribute towards an increased BMI and in turn to more health issues related to overweight and obesity. In this paper, the American Time Use Survey (ATUS) Eating & Health Module Files from 2014 survey is used to predict the BMI of people based on their income and through it, we try to establish a relationship between overweight/obese likely based on the income of the family. To do this analysis different machine learning algorithms will be used and finally a comparison of all the algorithms are done with the help of ROC curve.
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