Air pollution and hospital admissions for cardiorespiratory diseases in Iran: artificial neural network versus conditional logistic regression

Shakerkhatibi, Mohammad; Dianat, Iman; Jafarabadi, Mohammad Asghari; Azak, Razieh; Kazemi, Ahmad

doi:10.1007/s13762-015-0884-0

Cited by 27 publications

(15 citation statements)

References 36 publications

(61 reference statements)

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“…Extracted data from STEPs and NASBOD will be used to predict the MI mortality rate using ANN (25)(26)(27)(28)(29)(30). The modeling will be repeated 100 times to enhance the accuracy of the ANN model and eventually, the mean value of repeated results will be used to predict the future.…”

Section: Discussionmentioning

confidence: 99%

A A Scenario-based Modelling Study of the Prevention of Myocardial Infarction in Iran : A Scenario-based and Modeling Study on the Prevention of Heart Attack in Iran

Alizadeh

Gholipour

Dehnavieh

et al. 2020

ircmj

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Background and Objectives: Globally, cardiovascular disease (CVD) is the number one cause of mortality. In this regard, this study aimed to provide policies for the management of CVD by focusing on the reduction of myocardial infarction (MI) mortality rate in Iran. Materials and Methods: The sequential mixed methods design will be employed to foresight the prevalence of MI in Iran in the next 10 years. This study consists of five phases and in the first phase, the risk factors of cardiovascular disease will be investigated using a systematic review. In the second phase, the uncertainty and impact of those factors will be demonstrated by the experts. Moreover, the impact/uncertainty grid will be used to identify the drivers that are less important and critical uncertainties. In the third phase, the cross-impact matrix will be developed by Scenario wizard, and the scenario logic and the scenarios will be developed. Once the scenario logic is established, details can be added to the scenarios. The next phase consists of statistical estimations of the rate of mortality due to heart attack using artificial neural networks. Finally, the policies will be developed based on the opinions of the panel of experts. The initial results will be published in mid-2020. Results: This future study will develop policies to prevent from MI with scenario-based and modeling approaches. The findings can be useful for healthcare professionals and it can improve our understanding of the future of MI to enhance the management of MI patients. Conclusion: The obtained policies will help policymakers to make evidence-based decisions, re-design structures, and processes of healthcare interventions, and also plan to decrease MI mortality rate.

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Section: Discussionmentioning

confidence: 99%

A A Scenario-based Modelling Study of the Prevention of Myocardial Infarction in Iran : A Scenario-based and Modeling Study on the Prevention of Heart Attack in Iran

Alizadeh

Gholipour

Dehnavieh

et al. 2020

ircmj

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“…In this paper, the value of the ozone formation potential is higher than the recommended air quality limit (100 μg m -3 ) [52]. A high OFP value increases ozone generation and causes adverse health effects [53][54][55], which means that BTX compounds with a high MIR value show photochemical activity [42,56]. Note: MIR = maximum incremental activity; RWT = raw water tank; AST = ammonia stripping tower; A = anaerobic tank; O1 = anterior aerobic tank; O2 = posterior aerobic tank; ET = effluent tank.…”

Section: The Ofp Of Vocs In a Coking Wwtpmentioning

confidence: 99%

Distribution Characteristics of Volatile Organic Compounds and Contribution to Ozone Formation in a Coking Wastewater Treatment Plant

Zhang

Zang

Yan

et al. 2020

IJERPH

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Ozone pollution, which can be caused by photochemical reactions, has become a serious problem. The ozone formation potential (OFP) is used to describe the photochemical reactivity. Volatile organic compounds (VOCs) are main precursors of ozone formation, and wastewater treatment plants (WWTPs) are important sources of VOCs. Therefore, it is necessary to study the concentration level and OFP of VOCs from WWTPs. In this work, a coking WWTP with anaerobic-oxic-oxic (A/O/O) processes in Shaoguan city, Guangdong province, China, was selected to investigate the characteristics of VOCs at wastewater treatment areas and office areas. The OFP of VOCs was estimated by the maximum incremental reactivity (MIR) coefficient method. Results showed that 17 VOCs were detected, and the total concentration of VOCs was the highest at the raw water tank (857.86 μg m−3). The benzene series accounted for 69.0%–86.9% and was the main component of VOCs in the WWTP. Based on OFP data, the top six VOCs contributing most to the OFP were m-xylene, toluene, p-xylene, o-xylene, styrene, and benzene. This study provides field data and information on the environmental risk of VOCs for coking companies and environmental departments. We found that the priority control sources of VOCs were wastewater treatment units because of their larger OFP contributions.

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“…Recently, machine learning algorithms, which can solve the nonlinear relationship among multi-dimensional variables, have been shown to be effective in prediction, and are being used successfully in various healthcare applications, such as medical diagnosis [22,23] and disease risk prediction [24,25]. Nevertheless, only a very limited number of studies have attempted to adopt machinelearning based data-driven approaches to forecast the demand for healthcare services associated with environmental exposure, and these few studies predominately focused on the application of artificial neural network (ANN) [26][27][28][29]. For instance, Kassomenos et al [30] applied ANN and stepwise regression models to predict the daily number of hospital admissions for CVDs and respiratory diseases considering air pollution and meteorological conditions, and ANN performed better than the regression model.…”

Section: Introductionmentioning

confidence: 99%

Machine learning approaches to predict peak demand days of cardiovascular admissions considering environmental exposure

Qiu

Luo

et al. 2020

BMC Med Inform Decis Mak

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Background: Accumulating evidence has linked environmental exposure, such as ambient air pollution and meteorological factors, to the development and severity of cardiovascular diseases (CVDs), resulting in increased healthcare demand. Effective prediction of demand for healthcare services, particularly those associated with peak events of CVDs, can be useful in optimizing the allocation of medical resources. However, few studies have attempted to adopt machine learning approaches with excellent predictive abilities to forecast the healthcare demand for CVDs. This study aims to develop and compare several machine learning models in predicting the peak demand days of CVDs admissions using the hospital admissions data, air quality data and meteorological data in Chengdu, China from 2015 to 2017. Methods: Six machine learning algorithms, including logistic regression (LR), support vector machine (SVM), artificial neural network (ANN), random forest (RF), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM) were applied to build the predictive models with a unique feature set. The area under a receiver operating characteristic curve (AUC), logarithmic loss function, accuracy, sensitivity, specificity, precision, and F1 score were used to evaluate the predictive performances of the six models. Results: The LightGBM model exhibited the highest AUC (0.940, 95% CI: 0.900-0.980), which was significantly higher than that of LR (0.842, 95% CI: 0.783-0.901), SVM (0.834, 95% CI: 0.774-0.894) and ANN (0.890, 95% CI: 0.836-0.944), but did not differ significantly from that of RF (0.926, 95% CI: 0.879-0.974) and XGBoost (0.930, 95% CI: 0.878-0.982). In addition, the LightGBM has the optimal logarithmic loss function (0.218), accuracy (91.3%), specificity (94.1%), precision (0.695), and F1 score (0.725). Feature importance identification indicated that the contribution rate of meteorological conditions and air pollutants for the prediction was 32 and 43%, respectively.

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Air pollution and hospital admissions for cardiorespiratory diseases in Iran: artificial neural network versus conditional logistic regression

Cited by 27 publications

References 36 publications

A A Scenario-based Modelling Study of the Prevention of Myocardial Infarction in Iran : A Scenario-based and Modeling Study on the Prevention of Heart Attack in Iran

A A Scenario-based Modelling Study of the Prevention of Myocardial Infarction in Iran : A Scenario-based and Modeling Study on the Prevention of Heart Attack in Iran

Distribution Characteristics of Volatile Organic Compounds and Contribution to Ozone Formation in a Coking Wastewater Treatment Plant

Machine learning approaches to predict peak demand days of cardiovascular admissions considering environmental exposure

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