Comparing the Random Forest with the Generalized Additive Model to Evaluate the Impacts of Outdoor Ambient Environmental Factors on Scaffolding Construction Productivity

Background and objectives: Machine learning approaches using random forest have been effectively used to provide decision support in health and medical informatics. This is especially true when predicting variables associated with Medicare reimbursements. However, more work is needed to analyze and predict data associated with reimbursements through Medicare and Medicaid services for physical therapy practices in the United States. The key objective of this study is to analyze different machine learning models to predict key variables associated with Medicare standardized payments for physical therapy practices in the United States. Materials and Methods: This study employs five methods, namely, multiple linear regression, decision tree regression, random forest regression, K-nearest neighbors, and linear generalized additive model, (GAM) to predict key variables associated with Medicare payments for physical therapy practices in the United States. Results: The study described in this article adds to the body of knowledge on the effective use of random forest regression and linear generalized additive model in predicting Medicare Standardized payment. It turns out that random forest regression may have any edge over other methods employed for this purpose. Conclusions: The study provides a useful insight into comparing the performance of the aforementioned methods, while identifying a few intricate details associated with predicting Medicare costs while also ascertaining that linear generalized additive model and random forest regression as the most suitable machine learning models for predicting key variables associated with standardized Medicare payments.

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Application of a generalized additive model (GAM) to reveal relationships between environmental factors and distributions of pelagic fish and krill: a case study in Sendai Bay, Japan

ICES Journal of Marine Science ◽

10.1093/icesjms/fsp105 ◽

2009 ◽

Vol 66 (6) ◽

pp. 1417-1424 ◽

Cited By ~ 61

Author(s):

Hiroto Murase ◽

Hiroshi Nagashima ◽

Shiroh Yonezaki ◽

Ryuichi Matsukura ◽

Toshihide Kitakado

Keyword(s):

Environmental Factors ◽

Water Temperature ◽

Generalized Additive Model ◽

Additive Model ◽

Distribution Patterns ◽

Pelagic Fish ◽

Environmental Covariates ◽

Ammodytes Personatus ◽

Sendai Bay

Abstract Murase, H., Nagashima, H., Yonezaki, S., Matsukura, R., and Kitakado, T. 2009. Application of a generalized additive model (GAM) to reveal relationships between environmental factors and distributions of pelagic fish and krill: a case study in Sendai Bay, Japan. – ICES Journal of Marine Science, 66: 1417–1424. A generalized additive model (GAM) was applied to fishery-survey data to reveal the influences of environmental factors on the distribution patterns of Japanese anchovy (Engraulis japonicus), sand lance (Ammodytes personatus), and krill (Euphausia pacifica). Echosounder and physical-oceanographic data were collected in Sendai Bay, Japan, in spring 2005. A hierarchical model was used with two spatial strata: (i) presence and absence of each species; and (ii) biomass density of each species, given its presence; and six environmental covariates (surface water temperature, salinity, and chlorophyll, and near-seabed water temperature, salinity, and depth). The results indicate non-linear responses of the two indices to the environmental covariates. In addition, the biomasses estimated by the GAMs were comparable with estimates based on conventional, stratified-random sampling for each species. GAMs are very useful for (i) investigating the effects of environmental factors on the distributions of biological organisms, and (ii) predicting the distributions of animal densities in unsurveyed areas.

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Associations of Acute Exposure to Airborne Pollutants with COVID-19 Infection: Evidence from China

10.21203/rs.3.rs-273170/v1 ◽

2021 ◽

Author(s):

Bingqing Lu ◽

Na Wu ◽

Jiakui Jiang ◽

Xiang Li

Keyword(s):

Relative Humidity ◽

Environmental Factors ◽

Generalized Additive Model ◽

Additive Model ◽

Mitigation Strategies ◽

Atmospheric Pollutants ◽

Acute Exposure ◽

Airborne Pollutants ◽

Relative Risks ◽

Pollutant Concentrations

Abstract The outbreak of COVID-19, caused by SARS-CoV-2 has spread across many countries globally. Greatly limited study concerned the effect of airborne pollutants on COVID-19 infection, while exposure to airborne pollutants may harm human health. This paper aimed to examine the associations of acute exposure to ambient atmospheric pollutants to daily newly COVID-19 confirmed cases in 41 Chinese cities. Using a generalized additive model with Poisson distribution controlling for temperature and relative humidity, we evaluated the association between pollutant concentrations and daily COVID-19 confirmation at single-city level and multi-city level. We observed a 10 μg/m3 rise in levels of PM2.5 (lag 0−14), O3 (lag 0−1), SO2 (lag 0) and NO2 (lag 0−14) were positively associated with relative risks of 1.050 (95% CI: 1.028, 1.073), 1.011 (1.007, 1.015), 1.052 (1.022, 1.083) and 1.094 (1.028, 1.164) of daily newly confirmed cases, respectively. Further adjustment for other pollutants did not change the associations materially (excepting in the model for SO2). Our results indicated that COVID-19 incidence may be susceptible to airborne pollutants such as PM2.5, O3, SO2 and NO2, and mitigation strategies of environmental factors are required to prevent spreading.

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Effects of temporal and environmental factors on the fishing ground of Antarctic krill(Euphausia superba) in the northern Antarctic Peninsula based on generalized additive model

JOURNAL OF FISHERIES OF CHINA ◽

10.3724/sp.j.1231.2012.27709 ◽

2012 ◽

Vol 36 (12) ◽

pp. 1863

Author(s):

Guoping ZHU

Keyword(s):

Environmental Factors ◽

Antarctic Peninsula ◽

Antarctic Krill ◽

Generalized Additive Model ◽

Additive Model ◽

Euphausia Superba ◽

Fishing Ground ◽

Antarctic Krill Euphausia Superba

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CLIMATE-DRIVEN STATISTICAL MODELS AS EFECTIVE PREDICTIONS OF LOCAL DENGUE INDICENCE IN COSTA RICA: A GENERALIZED ADDITIVE MODEL AND RANDOM FOREST APPROACH

Revista de Matemática Teoría y Aplicaciones ◽

10.15517/rmta.v27i1.39931 ◽

2019 ◽

Vol 27 (1) ◽

pp. 1-21

Author(s):

PAOLA VÁSQUEZ ◽

ANTONIO LORÍA ◽

FABIO SÁNCHEZ ◽

LUIS ALBERTO BARBOZA

Keyword(s):

Public Health ◽

Relative Risk ◽

Costa Rica ◽

Random Forest ◽

Prediction Model ◽

Statistical Models ◽

Generalized Additive Model ◽

Additive Model ◽

Climate Data ◽

Tropical Country

Climate has been an important factor in shaping the distribution and incidence of dengue cases in tropical and subtropical countries. In Costa Rica, a tropical country with distinctive micro-climates, dengue has been endemic since its introduction in 1993, inflicting substantial economic, social, and public health repercussions. Using the number of dengue reported cases and climate data from 2007-2017, we fitted a prediction model applying a Generalized Additive Model (GAM) and Random Forest (RF) approach, which allowed us to retrospectively predict the relative risk of dengue in five climatological diverse municipalities around the country.

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