Comparing Classic Regression With Credit Scorecard Model for Predicting Sick Building Syndrome Risk: A Machine Learning Perspective in Environmental Assessment

Comparing Classic Regression With Credit Scorecard Model for Predicting Sick Building Syndrome Risk: A Machine Learning Perspective in Environmental Assessment Publisher

Hosseini MR^{1, 2} ; Godini H² ; Fouladifard R^{3, 4} ; Ghanami Z⁵ ; Ghafoory N¹ ; Balali M⁶ ; Faridan M⁷

Show Affiliations

1. Student Research Committee, School of Health, Alborz University of Medical Sciences, Karaj, Iran
2. Department of Environmental Health Engineering, School of Health, Alborz University of Medical Sciences, Karaj, Iran
3. Research Center for Environmental Pollutants, Department of Environmental Health Engineering, Faculty of Health, Qom University of Medical Sciences, Qom, Iran
4. Environmental Health Research Center, School of Health and Nutrition, Lorestan University of Medical Sciences, Khorramabad, Iran
5. Student Research Committee, School of Health, Semnan University of Medical Sciences, Damghan, Iran
6. Department of Environmental Health Engineering, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran
7. Environmental Health Research Center, Department of Occupational Health and Safety at Work Engineering, Lorestan University of Medical Sciences, Khorramabad, Iran

Source: Building and Environment Published:2024

Abstract

Quarantine policy for COVID-19 control brings outdoor risks to indoor, leading to unintended exposure to sick building syndrome (SBS) symptoms. The aim of this study was to establish a comparative framework for the application of multiple logistic regression (classic regression) and credit scorecard models based on Logistic Regression (logit) and Decision Tree (DT) models in predicting the risk of developing SBS among citizens of Alborz province in Iran. The results of the classic regression indicated that the number of residents in the home (OR = 1.127, 95% CI: 1.017–1.760), were shown to have a significant association with total SBS symptoms. In the credit scorecard model utilizing the logit model, the variable with the greatest predictive significance for total SBS as well as general, and mucosal symptoms was work status. Furthermore, in the credit scorecard model based on DT model, the levels of PM2.5 and PM10, were the most significant predictors for the total SBS and dermal symptoms, respectively. As such, ozone (O3) emerged as the most influential factor in predicting both general and mucosal symptoms. As credit scoring, the greatest base score (109.86) was associated with mucosal symptoms, while the lowest base score (82.10) was associated with general symptoms. In conclusion, the DT model exhibited superior performance as a scoring model compared to the logit model and classic regression, as evidenced by its higher performance indexes. As such, only dermal symptoms exhibited mean scores higher than the base scores, suggesting that the overall quality of the indoor environments was generally poor. © 2024 Elsevier Ltd

2. Outdoor Fine and Coarse Particles and Hospital Admissions for Cardiovascular Diseases: A Large-Scale Case-Crossover Study, Air Quality, Atmosphere and Health (2022)

3. Evaluation of Building Washing Machines As an Extreme Environment for Potentially Pathogenic Fungi, Scientific Reports (2023)

4. Correlation Between Air Pollution and Hospitalization Due to Myocardial Infarction, ARYA Atherosclerosis (2019)

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Style	Citing Format
MLA	Hosseini MR, et al.. "Comparing Classic Regression With Credit Scorecard Model for Predicting Sick Building Syndrome Risk: A Machine Learning Perspective in Environmental Assessment." Building and Environment, vol. 253, no. , 2024, pp. -.
APA	Hosseini MR, Godini H, Fouladifard R, Ghanami Z, Ghafoory N, Balali M, Faridan M (2024). Comparing Classic Regression With Credit Scorecard Model for Predicting Sick Building Syndrome Risk: A Machine Learning Perspective in Environmental Assessment. Building and Environment, 253(), -.
Chicago	Hosseini MR, Godini H, Fouladifard R, Ghanami Z, Ghafoory N, Balali M, Faridan M. "Comparing Classic Regression With Credit Scorecard Model for Predicting Sick Building Syndrome Risk: A Machine Learning Perspective in Environmental Assessment." Building and Environment 253, no. (2024): -.
Harvard	Hosseini MR et al. (2024) 'Comparing Classic Regression With Credit Scorecard Model for Predicting Sick Building Syndrome Risk: A Machine Learning Perspective in Environmental Assessment', Building and Environment, 253(), pp. -.
Vancouver	Hosseini MR, Godini H, Fouladifard R, Ghanami Z, Ghafoory N, Balali M, et al.. Comparing Classic Regression With Credit Scorecard Model for Predicting Sick Building Syndrome Risk: A Machine Learning Perspective in Environmental Assessment. Building and Environment. 2024;253():-.
BibTex	@article{ author = {Hosseini MR and Godini H and Fouladifard R and Ghanami Z and Ghafoory N and Balali M and Faridan M}, title = {Comparing Classic Regression With Credit Scorecard Model for Predicting Sick Building Syndrome Risk: A Machine Learning Perspective in Environmental Assessment}, journal = {Building and Environment}, volume = {253}, number = {}, pages = {-}, year = {2024} }
RIS	TY - JOUR AU - Hosseini MR AU - Godini H AU - Fouladifard R AU - Ghanami Z AU - Ghafoory N AU - Balali M AU - Faridan M TI - Comparing Classic Regression With Credit Scorecard Model for Predicting Sick Building Syndrome Risk: A Machine Learning Perspective in Environmental Assessment JO - Building and Environment VL - 253 IS - SP - EP - PY - 2024 ER -

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