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首页> 外文期刊>Applied Geography >Spatial estimation of chronic respiratory diseases based on machine learning procedures-an approach using remote sensing data and environmental variables in quito, Ecuador
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Spatial estimation of chronic respiratory diseases based on machine learning procedures-an approach using remote sensing data and environmental variables in quito, Ecuador

机译：基于机器学习程序的慢性呼吸疾病的空间估算 - 一种使用遥感数据和基多，厄瓜多尔环境变量的方法

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摘要

Over the last few years, the use of remote sensing data in different applications such as estimation of air pollution concentration and health applications has become very popular and new. Thus, some studies have established a possible relationship between environmental variables and respiratory health parameters. This study proposes to estimate the prevalence of Chronic Respiratory Diseases, where there is a relationship between remote sensing data (Landsat 8) and environmental variables (air pollution and meteorological data) to determine the number of hospital discharges of patients with chronic respiratory diseases in Quito, Ecuador, between 2013 and 2017. The main objective of this study is to establish and evaluate an alternative LUR model that is capable of estimate the prevalence of chronic respiratory diseases, in contrast with traditional LUR models, which typically assess air pollutants. Moreover, this study also evaluates different analytic techniques (multiple linear regression, multilayer perceptron, support vector regression, and random forest regression) that often form the basis of spatial models. The results show that machine learning techniques, such as support vector machine, are the most effective in computing such models, presenting the lowest root-mean-square error (RMSE). Additionally, in this study, we show that the most significant remote sensing predictors are the blue and infrared bands. Our proposed model is a spatial modeling approach that is capable of determining the prevalence of chronic respiratory diseases in the city of Quito, which can serve as a useful tool for health authorities in policy- and decision-making.
机译：在过去几年中，在不同的应用中使用遥感数据，例如空气污染浓度和健康应用的估计变得非常受欢迎和新的。因此，一些研究已经建立了环境变量和呼吸系统健康参数之间的可能关系。本研究提出估计慢性呼吸系统疾病的患病率，遥感数据（LANDSAT 8）和环境变量（空气污染和气象数据）之间存在关系，以确定基多慢性呼吸系统患者的医院放电次数，厄瓜多尔，在2013年和2017年之间。本研究的主要目标是建立和评估能够估算慢性呼吸道疾病患病率的替代LUR模型，与传统的LUR模型相比，通常评估空气污染物。此外，该研究还评估了通常构成空间模型的基础的不同分析技术（多层回归，多层的回归，支持向量回归和随机森林回归和随机森林回归。结果表明，机器学习技术，如支持向量机，在计算这些模型中是最有效的，呈现最低的根均方误差（RMSE）。此外，在本研究中，我们表明最重要的遥感预测因子是蓝色和红外条带。我们所提出的模型是一种空间建模方法，能够确定基多市慢性呼吸系统疾病的患病率，可以作为政策和决策中的卫生当局的有用工具。

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