Unhealthy Behaviors, Prevention Measures, and Neighborhood Cardiovascular Health: A Machine Learning Approach
Unhealthy Behaviors, Prevention Measures, and Neighborhood Cardiovascular Health: A Machine Learning Approach
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DOI：
10.1097/phh.0000000000000817
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发表时间：
2019-01-01
影响因子：
3.3
通讯作者：
Liu, Bian
中科院分区：
文献类型：
--
作者：
Li, Yan;Liu, Shelley H.;Liu, Bian
这项研究确定并排名了美国社区水平的心血管健康预测因素。我们合并了500个城市数据和2011-2015年美国社区调查，创建了一个新的数据集，其中包括美国28000多个人口普查区的社会人口特征，健康行为，预防措施和心血管健康结果。我们使用随机森林对冠心病和中风的预测因子进行排名。对于冠心病，前5位的预测因素是服用药物控制高血压的流行率，酗酒，年龄在65岁或以上，缺乏闲暇时间的体力活动，肥胖。对于中风，前5位的预测因素是肥胖的流行，缺乏闲暇时间的身体活动，服用高血压药物，黑人和酗酒。机器学习方法有可能为决策者提供有关社区层面重要资源分配决策的信息。
This study identifies and ranks predictors of cardiovascular health at the neighborhood level in the United States. We merged the 500 Cities Data and the 2011-2015 American Community Survey to create a new data set that includes sociodemographic characteristics, health behaviors, prevention measures, and cardiovascular health outcomes for more than 28 000 census tracts in the United States. We used random forest to rank predictors of coronary heart disease and stroke. For coronary heart disease, the top 5 ordered predictors were the prevalence of taking medicine for high blood pressure control, binge drinking, being aged 65 years or older, lack of leisure-time physical activity, and obesity. For stroke, the top 5 ordered predictors were the prevalence of obesity, lack of leisure-time physical activity, taking medicine for high blood pressure, being black, and binge drinking. Machine learning approaches have the potential to inform policy makers on important resource allocation decisions at the neighborhood level.