Tree-Based Machine Learning to Identify and Understand Major Determinants for Stroke at the Neighborhood Level.
Tree-Based Machine Learning to Identify and Understand Major Determinants for Stroke at the Neighborhood Level.
复制标题
基于树的机器学习来识别和理解邻居级别的中风的主要决定因素。
DOI：
10.1161/jaha.120.016745
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发表时间：
2020-11-17
影响因子：
5.4
通讯作者：
Li Y
中科院分区：
文献类型：
--
作者：
Hu L;Liu B;Ji J;Li Y
中风是一种主要的心血管疾病，在美国造成重大的健康和经济负担。邻里社区为基础的干预措施已被证明是有效的和成本效益在预防心血管疾病。缺乏强有力的研究来确定心血管疾病的关键决定因素和社区水平的潜在影响机制。我们的目标是为社区心血管健康研究提供证据基础。我们通过整合4种类型的潜在预测因子，包括来自多个数据源的不健康行为，预防措施，社会人口因素和环境措施，在人口普查区水平上创建了一个新的社区健康数据集。我们使用了4种基于树的机器学习技术来识别预测社区水平中风患病率的最关键的社区水平因素，并比较了它们在变量选择方面的预测性能。我们使用贝叶斯线性回归模型进一步量化了确定的决定因素对卒中患病率的影响。在我们的方法确定的5个最重要的预测因素中，低体力活动的患病率较高，老年人的比例较大，非西班牙裔黑人的比例较高，臭氧水平较高与社区水平的中风患病率较高相关。较高的家庭收入中位数与较低的患病率有关。最重要的交互作用项显示，老龄化和低体力活动对社区卒中患病率的不良影响加剧。基于树的机器学习通过以不可知的、数据驱动的和可重复的方式从广泛的因素中发现最重要的决定因素，从而深入了解社区心血管健康的潜在驱动因素。确定的主要决定因素和互动机制可用于优先考虑和分配资源，以优化卒中预防的社区干预措施。
Stroke is a major cardiovascular disease that causes significant health and economic burden in the United States. Neighborhood community‐based interventions have been shown to be both effective and cost‐effective in preventing cardiovascular disease. There is a dearth of robust studies identifying the key determinants of cardiovascular disease and the underlying effect mechanisms at the neighborhood level. We aim to contribute to the evidence base for neighborhood cardiovascular health research. We created a new neighborhood health data set at the census tract level by integrating 4 types of potential predictors, including unhealthy behaviors, prevention measures, sociodemographic factors, and environmental measures from multiple data sources. We used 4 tree‐based machine learning techniques to identify the most critical neighborhood‐level factors in predicting the neighborhood‐level prevalence of stroke, and compared their predictive performance for variable selection. We further quantified the effects of the identified determinants on stroke prevalence using a Bayesian linear regression model. Of the 5 most important predictors identified by our method, higher prevalence of low physical activity, larger share of older adults, higher percentage of non‐Hispanic Black people, and higher ozone levels were associated with higher prevalence of stroke at the neighborhood level. Higher median household income was linked to lower prevalence. The most important interaction term showed an exacerbated adverse effect of aging and low physical activity on the neighborhood‐level prevalence of stroke. Tree‐based machine learning provides insights into underlying drivers of neighborhood cardiovascular health by discovering the most important determinants from a wide range of factors in an agnostic, data‐driven, and reproducible way. The identified major determinants and the interactive mechanism can be used to prioritize and allocate resources to optimize community‐level interventions for stroke prevention.