Spatial Modeling of Asthma-Prone Areas Using Remote Sensing and Ensemble Machine Learning Algorithms

Seyed Vahid Razavi-Termeh,Soo-Mi Choi,Abolghasem Sadeghi-Niaraki

doi:10.3390/rs13163222

Seyed Vahid Razavi-Termeh, Soo-Mi Choi + Show 1 more

Open Access

https://doi.org/10.3390/rs13163222

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Abstract

In this study, asthma-prone area modeling of Tehran, Iran was provided by employing three ensemble machine learning algorithms (Bootstrap aggregating (Bagging), Adaptive Boosting (AdaBoost), and Stacking). First, a spatial database was created with 872 locations of asthma patients and affecting factors (particulate matter (PM10 and PM2.5), ozone (O3), sulfur dioxide (SO2), carbon monoxide (CO), nitrogen dioxide (NO2), rainfall, wind speed, humidity, temperature, distance to street, traffic volume, and a normalized difference vegetation index (NDVI)). We created four factors using remote sensing (RS) imagery, including air pollution (O3, SO2, CO, and NO2), altitude, and NDVI. All criteria were prepared using a geographic information system (GIS). For modeling and validation, 70% and 30% of the data were used, respectively. The weight of evidence (WOE) model was used to assess the spatial relationship between the dependent and independent data. Finally, three ensemble algorithms were used to perform asthma-prone areas mapping. According to the Gini index, the most influential factors on asthma occurrence were distance to the street, NDVI, and traffic volume. The area under the curve (AUC) of receiver operating characteristic (ROC) values for the AdaBoost, Bagging, and Stacking algorithms was 0.849, 0.82, and 0.785, respectively. According to the findings, the AdaBoost algorithm outperforms the Bagging and Stacking algorithms in spatial modeling of asthma-prone areas.

Highlights

Asthma is a chronic and inflammatory condition of the airways that affects more than 300 million people worldwide
A combination of geographic information system (GIS), remote sensing (RS), and ensemble machine learning algorithms were employed in this work to propose a strategy for the prevention and management of asthma in urban areas
The results showed that the ensemble machine learning algorithms have good accuracy in modeling asthma-prone areas, where the AdaBoost algorithm showed higher accuracy than the other two algorithms

Summary

Introduction

Asthma is a chronic and inflammatory condition of the airways that affects more than 300 million people worldwide. According to a report by the Global Initiative for Asthma (GINA), this number is expected to reach 400 million by 2025 [1,2]. The death rate from asthma is so high that it kills 250,000 people annually worldwide [3]. Asthma prevalence has been rising globally in recent decades. It tends to haunt a patient for the rest of their life [2,4]. There is no definitive cure for asthma, but it can be controlled and managed [5], and in this case, the risk of asthma attacks and resulting mortality is reduced. Asthma is a reversible airway obstruction and bronchospasm condition that affects the lungs [6]

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