Title
Predictive Modelling of PM2.5 Impact on Pulmonary Function Among Indian Textile Workers Diagnosed with Chronic Obstructive Pulmonary Disease: A Machine Learning-Based Study
Authors
Shankar S, Abbas G, Chander Prakash, Manikanadan M, Sathishkumar V E
Abstract
The work addresses the significant health risks posed by respiratory issues like chronic obstructive pulmonary diseases (COPD) among textile industry workers. The study assessed the performance of various machine learning algorithms, including Support Vector Machines (SVM), Decision Trees, Random Forest, and Linear Regression, in forecasting PM2.5 concentrations and their impact on pulmonary function indices (FVC, FEV1, and PEFR) among textile workers with COPD. Performance results indicated that Decision Tree and Random Forest models exhibited superior performance in predicting PM2.5 concentrations, with high accuracy, precision, recall, and F1 scores (99.9%). Additionally, higher PM2.5 levels were associated with decreased pulmonary function, with Decision Tree model showing the highest R2 value (0.972) and lowest RMSE (0.458) and MAE (0.573), emphasizing its robust performance in capturing PM2.5 variations. The use of machine learning algorithms and real-time IAQ monitoring data holds promise for improving the early detection and management of respiratory exacerbations among textile workers.
Keywords
Indoor Air Quality; Machine Learning; Pulmonary function; PM2.5; Textile Industry
Full Text
References
[1] Y. Bian, S. Wang, L. Zhang, and C. Chen, "Influence of fiber diameter, filter thickness, and packing
density on PM2. 5 removal efficiency of electrospun nanofiber air filters for indoor applications,"
Building and Environment, vol. 170, p. 106628, 2020.
[2] L. Y. Ben Porath, "Clinical, functional and biomarkers criteria for the diagnosis of asthma-chronic
obstructive pulmonary diseases overlap (ACO)," 2021.
[3] F. o. I. R. Societies, The global impact of respiratory disease. European Respiratory Society, 2017.
[4] M. MacLeod et al., "Chronic obstructive pulmonary disease exacerbation fundamentals: Diagnosis,
treatment, prevention and disease impact," Respirology, vol. 26, no. 6, pp. 532-551, 2021.
[5] R.-R. Duan, K. Hao, and T. Yang, "Air pollution and chronic obstructive pulmonary disease," Chronic
diseases and translational medicine, vol. 6, no. 04, pp. 260-269, 2020.
[6] A. W. CDC, "Centers for disease control and prevention," ed, 2020.
[7] S. Subramaniam, A. Ganesan, N. Raju, and C. Prakash, "Investigation of indoor air quality and
pulmonary function status among power loom industry workers in Tamil Nadu, South India," Air
Quality, Atmosphere & Health, vol. 17, no. 1, pp. 215-230, 2024.
[8] N. Parveen, L. Siddiqui, M. N. Sarif, M. S. Islam, N. Khanam, and S. Mohibul, "Industries in Delhi:
Air pollution versus respiratory morbidities," Process Safety and Environmental Protection, vol. 152,
pp. 495-512, 2021.
[9] S. Subramaniam et al., "Impact of cotton dust, endotoxin exposure, and other occupational health risk
due to indoor pollutants on textile industry workers in low and middle-income countries," Journal of
Air Pollution and Health, 2024.
[10] F. Parvin, S. Islam, S. I. Akm, Z. Urmy, S. Ahmed, and A. Islam, "A study on the solutions of
environment pollutions and worker’s health problems caused by textile manufacturing operations,"
Biomed. J. Sci. Tech. Res, vol. 28, no. 4, pp. 21831-21844, 2020.
[11] V. V. Tran, D. Park, and Y.-C. Lee, "Indoor air pollution, related human diseases, and recent trends in
the control and improvement of indoor air quality," International journal of environmental research
and public health, vol. 17, no. 8, p. 2927, 2020.
[12] S. Raju, T. Siddharthan, and M. C. McCormack, "Indoor air pollution and respiratory health," Clinics
in chest medicine, vol. 41, no. 4, pp. 825-843, 2020.
[13] J. A. Salomon et al., "The US COVID-19 Trends and Impact Survey: Continuous real-time
measurement of COVID-19 symptoms, risks, protective behaviors, testing, and vaccination,"
Proceedings of the National Academy of Sciences, vol. 118, no. 51, p. e2111454118, 2021.
[14] S. J. Hadeed, M. K. O'rourke, J. L. Burgess, R. B. Harris, and R. A. Canales, "Imputation methods for
addressing missing data in short-term monitoring of air pollutants," Science of the Total Environment,
vol. 730, p. 139140, 2020.
[15] K. D. Michaux et al., "IMplementing Predictive Analytics towards efficient COPD Treatments
(IMPACT): protocol for a stepped-wedge cluster randomized impact study," Diagnostic and
Prognostic Research, vol. 7, no. 1, p. 3, 2023.
[16] S. Saha, S. Bhattacharjee, B. Bera, and E. Haque, "Drivers of High Concentration and Dispersal of
PM10 and PM2. 5 in the Eastern Part of Chhota Nagpur Plateau, India, Investigated Through
HYSPLIT Model and Improvement of Environmental Health Quality," Environmental Quality
Management, vol. 34, no. 1, p. e22299, 2024.
[17] S. Subramaniam et al., "Artificial intelligence technologies for forecasting air pollution and human
health: A narrative review," Sustainability, vol. 14, no. 16, p. 9951, 2022.
[18] T. Zeng, L. Xu, Y. Liu, R. Liu, Y. Luo, and Y. Xi, "A hybrid optimization prediction model for PM2. 5
based on VMD and deep learning," Atmospheric Pollution Research, p. 102152, 2024.
[19] K. Santosh and L. Gaur, Artificial intelligence and machine learning in public healthcare:
Opportunities and societal impact. Springer Nature, 2022.
[20] Y. Feng, Y. Wang, C. Zeng, and H. Mao, "Artificial intelligence and machine learning in chronic
airway diseases: focus on asthma and chronic obstructive pulmonary disease," International journal of
medical sciences, vol. 18, no. 13, p. 2871, 2021.
[21] L. Luo et al., "Using machine learning approaches to predict high-cost chronic obstructive pulmonary
disease patients in China," Health informatics journal, vol. 26, no. 3, pp. 1577-1598, 2020.
[22] D. Spathis and P. Vlamos, "Diagnosing asthma and chronic obstructive pulmonary disease with
machine learning," Health informatics journal, vol. 25, no. 3, pp. 811-827, 2019.
[23] H. Joumaa, R. Sigogne, M. Maravic, L. Perray, A. Bourdin, and N. Roche, "Artificial intelligence to
differentiate asthma from COPD in medico-administrative databases," BMC Pulmonary Medicine, vol.
22, no. 1, p. 357, 2022.
PM2.5 Prediction on Pulmonary Function among Indian Textile Workers using Machine Learning
12
[24] R. Stoean and C. Stoean, "Modeling medical decision making by support vector machines, explaining
by rules of evolutionary algorithms with feature selection," Expert Systems with Applications, vol. 40,
no. 7, pp. 2677-2686, 2013.
[25] V. G. Costa and C. E. Pedreira, "Recent advances in decision trees: An updated survey," Artificial
Intelligence Review, vol. 56, no. 5, pp. 4765-4800, 2023.
[26] I. D. Mienye, Y. Sun, and Z. Wang, "An improved ensemble learning approach for the prediction of
heart disease risk," Informatics in Medicine Unlocked, vol. 20, p. 100402, 2020.
[27] J. V. Tu, "Advantages and disadvantages of using artificial neural networks versus logistic regression
for predicting medical outcomes," Journal of clinical epidemiology, vol. 49, no. 11, pp. 1225-1231,
1996.
[28] J. L. Amaral, A. J. Lopes, A. C. Faria, and P. L. Melo, "Machine learning algorithms and forced
oscillation measurements to categorise the airway obstruction severity in chronic obstructive
pulmonary disease," Computer methods and programs in biomedicine, vol. 118, no. 2, pp. 186-197,
2015.
[29] D. Sanchez-Morillo, M. A. Fernandez-Granero, and A. L. Jiménez, "Detecting COPD exacerbations
early using daily telemonitoring of symptoms and k-means clustering: a pilot study," Medical &
biological engineering & computing, vol. 53, pp. 441-451, 2015.
[30] F. Wu et al., "A novel hybrid model for hourly PM2. 5 prediction considering air pollution factors,
meteorological parameters and GNSS-ZTD," Environmental Modelling & Software, vol. 167, p.
105780, 2023.
[31] R. Nazir and M. H. Shah, "Evaluation of air quality and health risks associated with trace elements in
respirable particulates (PM2. 5) from Islamabad, Pakistan," Environmental Monitoring and
Assessment, vol. 195, no. 10, p. 1182, 2023.
[32] Y. Ishigaki, S. Yokogawa, K. Shimazaki, T.-T. Win-Shwe, and E. Irankunda, "Assessing personal
PM2. 5 exposure using a novel neck-mounted monitoring device in rural Rwanda," Environmental
Monitoring and Assessment, vol. 196, no. 10, p. 935, 2024.
[33] A. Hussain, H.-E. Choi, H.-J. Kim, S. Aich, M. Saqlain, and H.-C. Kim, "Forecast the exacerbation in
patients of chronic obstructive pulmonary disease with clinical indicators using machine learning
techniques," Diagnostics, vol. 11, no. 5, p. 829, 2021.
[34] B. E. Himes, Y. Dai, I. S. Kohane, S. T. Weiss, and M. F. Ramoni, "Prediction of chronic obstructive
pulmonary disease (COPD) in asthma patients using electronic medical records," Journal of the
American Medical Informatics Association, vol. 16, no. 3, pp. 371-379, 2009.
[35] L.-P. Boulet, H. K. Reddel, E. Bateman, S. Pedersen, J. M. FitzGerald, and P. M. O'Byrne, "The global
initiative for asthma (GINA): 25 years later," European Respiratory Journal, vol. 54, no. 2, 2019.
[36] R. Guido, S. Ferrisi, D. Lofaro, and D. Conforti, "An Overview on the Advancements of Support
Vector Machine Models in Healthcare Applications: A Review," Information, vol. 15, no. 4, p. 235,
2024.
[37] X. Zhu, X. Hu, L. Yang, W. Pedrycz, and Z. Li, "A Development of Fuzzy Rule-based Regression
Models through Using Decision Trees," IEEE Transactions on Fuzzy Systems, 2024.
[38] T. Kavzoglu and A. Teke, "Predictive Performances of ensemble machine learning algorithms in
landslide susceptibility mapping using random forest, extreme gradient boosting (XGBoost) and
natural gradient boosting (NGBoost)," Arabian Journal for Science and Engineering, vol. 47, no. 6, pp.
7367-7385, 2022.
[39] J. L. M. do Amaral and P. L. de Melo, "Clinical decision support systems to improve the diagnosis and
management of respiratory diseases," in Artificial intelligence in precision health: Elsevier, 2020, pp.
359-391.
[40] D. Krstinić, M. Braović, L. Šerić, and D. Božić-Štulić, "Multi-label classifier performance evaluation
with confusion matrix," Computer Science & Information Technology, vol. 1, pp. 1-14, 2020.
[41] S. Orozco-Arias, J. S. Piña, R. Tabares-Soto, L. F. Castillo-Ossa, R. Guyot, and G. Isaza, "Measuring
performance metrics of machine learning algorithms for detecting and classifying transposable
elements," Processes, vol. 8, no. 6, p. 638, 2020.
[42] Ž. Vujović, "Classification model evaluation metrics," International Journal of Advanced Computer
Science and Applications, vol. 12, no. 6, pp. 599-606, 2021.
[43] B. Kapusuzoglu and S. Mahadevan, "Information fusion and machine learning for sensitivity analysis
using physics knowledge and experimental data," Reliability Engineering & System Safety, vol. 214, p.
107712, 2021.
[44] D. Chicco and G. Jurman, "The advantages of the Matthews correlation coefficient (MCC) over F1
score and accuracy in binary classification evaluation," BMC genomics, vol. 21, pp. 1-13, 2020.
PM2.5 Prediction on Pulmonary Function among Indian Textile Workers using Machine Learning
13
[45] G. Westergaard, U. Erden, O. A. Mateo, S. M. Lampo, T. C. Akinci, and O. Topsakal, "Time Series
Forecasting Utilizing Automated Machine Learning (AutoML): A Comparative Analysis Study on
Diverse Datasets," Information, vol. 15, no. 1, p. 39, 2024.
[46] D. S. Khafaga, A. Ibrahim, S. Towfek, and N. Khodadadi, "Data Mining Techniques in Predictive
Medicine: An Application in hemodynamic prediction for abdominal aortic aneurysm disease," Journal
of Artificial Intelligence and Metaheuristics, vol. 5, no. 1, pp. 29-37, 2023.
[47] O. A. Ejohwomu et al., "Modelling and forecasting temporal PM2. 5 concentration using ensemble
machine learning methods," Buildings, vol. 12, no. 1, p. 46, 2022.
[48] I. K. Umar, V. Nourani, and H. Gökçekuş, "A novel multi-model data-driven ensemble approach for
the prediction of particulate matter concentration," Environmental Science and Pollution Research, vol.
28, no. 36, pp. 49663-49677, 2021.
[49] Y. Huang, M. Bao, J. Xiao, Z. Qiu, and K. Wu, "Effects of PM2. 5 on cardio-pulmonary function
injury in open manganese mine workers," International journal of environmental research and public
health, vol. 16, no. 11, p. 2017, 2019.
[50] J. De Hartog et al., "Lung function and indicators of exposure to indoor and outdoor particulate matter
among asthma and COPD patients," Occupational and environmental medicine, vol. 67, no. 1, pp. 2-
10, 2010.
[51] R. J. Laumbach et al., "Personal interventions for reducing exposure and risk for outdoor air pollution:
an official American Thoracic Society workshop report," Annals of the American Thoracic Society, vol.
18, no. 9, pp. 1435-1443, 2021.
[52] M.-J. Ting, Y.-H. Tsai, S.-P. Chuang, P.-H. Wang, and S.-L. Cheng, "Is PM2. 5 associated with
emergency department visits for mechanical ventilation in acute exacerbation of chronic obstructive
pulmonary disease?," The American Journal of Emergency Medicine, vol. 50, pp. 566-573, 2021.