Intelligent air pollution prediction algorithm-based optimized random forest regression for reducing asthmatic attacks
,
Abstract
Introduction: Air pollution can trigger the attack in asthmatic patients if uncontrolled. Previous works focused on controlling pollution by proposing algorithms to predict air pollution. While these prediction algorithms save
patients from attack triggers, they have limitations such as prediction accuracy, mathematical complexity, and lack of adequate patient notification systems.
Materials and methods: This study proposed a novel Intelligent Air Pollution Prediction (IAPP) algorithm based on optimizing Random Forest Regression (RFR) to predict air pollution and send an alert message to the patient and hospital in real time. Meanwhile, IAPP utilized reliable data from Internet of Things (IoT)-based air pollution detection nodes. The performance of IAPP was evaluated in a real-world environment during the peak pollutant season to test the prediction accuracy of air pollution.
Results: Results showed that the proposed IAPP achieved a high prediction accuracy of 99.98% with an R squared value of 0.99. This demonstrated that the IAPP algorithm based on the RFR model can effectively protect asthmatic patients from attack triggers.
Conclusion: As a result, the IAPP algorithm reduces hospital visits during high pollution and enables patients to complete their daily activities without obstacles or absence.
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4. Brook RD, Rajagopalan S, Al-Kindi S. Public health relevance of US EPA air quality index activity recommendations. JAMA Network Open. 2024 Apr 1;7(4):e245292.
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9. Veiga T, Munch-Ellingsen A, Papastergiopoulos C, Tzovaras D, Kalamaras I, Bach K, Votis K, Akselsen S. From a low-cost air quality sensor network to decision support services: Steps towards data calibration and service development. Sensors. 2021 May 5;21(9):3190.
10. Anan SR, Hossain MA, Milky MZ, Khan MM, Masud M, Aljahdali S. [Retracted] Research and Development of an IoT‐Based Remote Asthma Patient Monitoring System. Journal of Healthcare Engineering. 2021;2021(1):2192913.
11. Ra HK, Salekin A, Yoon HJ, Kim J, Nirjon S, Stone DJ, Kim S, Lee JM, Son SH, Stankovic JA. Asthmaguide: an asthma monitoring and advice ecosystem. In2016 IEEE Wireless Health (WH) 2016 Oct 25 (pp. 1-8). IEEE.
12. Khatri KL, Tamil LS. Early detection of peak demand days of chronic respiratory diseases emergency department visits using artificial neural networks. IEEE journal of biomedical and health informatics. 2017 Apr 26;22(1):285-90.
13. Jaimini U, Banerjee T, Romine W, Thirunarayan K, Sheth A, Kalra M. Investigation of an indoor air quality sensor for asthma management in children. IEEE sensors letters. 2017 Apr 6;1(2):1-4.
14. Hosseini A, Buonocore CM, Hashemzadeh S, Hojaiji H, Kalantarian H, Sideris C, Bui AA, King CE, Sarrafzadeh M. HIPAA compliant wireless sensing smartwatch application for the self-management of pediatric asthma. In2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN) 2016 Jun 14 (pp. 49-54). IEEE.
15. Kaffash-Charandabi N, Alesheikh AA, Sharif M. A ubiquitous asthma monitoring framework based on ambient air pollutants and individuals’ contexts. Environmental Science and Pollution Research. 2019 Mar 20;26(8):7525-39.
16. Liu Y, Pan J, Zhang H, Shi C, Li G, Peng Z, Ma J, Zhou Y, Zhang L. Short-term exposure to ambient air pollution and asthma mortality. American journal of respiratory and critical care medicine. 2019 Jul 1;200(1):24-32.
17. Ding L, Zhu D, Peng D, Zhao Y. Air pollution and asthma attacks in children: A case–crossover analysis in the city of Chongqing, China. Environmental pollution. 2017 Jan 1;220:348-53.
18. Shin SW, Bae DJ, Park CS, Lee JU, Kim RH, Kim SR, Chang HS, Park JS. Effects of air pollution on moderate and severe asthma exacerbations. Journal of Asthma. 2020 Aug 2;57(8):875-85.
19. Han CH, Pak H, Chung JH. Short-term effects of exposure to particulate matter and air pollution on hospital admissions for asthma and chronic obstructive pulmonary disease in Gyeonggi-do, South Korea, 2007–2018. Journal of Environmental Health Science and Engineering. 2021 Dec;19:1535-41.
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21. Sharma AK, Saini S, Chhabra P, Chhabra SK, Ghosh C, Baliyan P. Air pollution and weather as the determinants of acute attacks of asthma: Spatiotemporal approach. Indian Journal of Public Health. 2020 Apr 1;64(2):124-9.
22. P Kortoçi P, Motlagh NH, Zaidan MA, Fung PL, Varjonen S, Rebeiro-Hargrave A, Niemi JV, Nurmi P, Hussein T, Petäjä T, Kulmala M. Air pollution exposure monitoring using portable low-cost air quality sensors. Smart health. 2022 Mar 1;23:100241.
23. Anastasiou E, Vilcassim MR, Adragna J, Gill E, Tovar A, Thorpe LE, Gordon T. Feasibility of low-cost particle sensor types in long-term indoor air pollution health studies after repeated calibration, 2019–2021. Scientific reports. 2022 Aug 26;12(1):14571.
24. Singh VK, Singh C, Raza H. Event classification and intensity discrimination for forest fire inference with IoT. IEEE Sensors Journal. 2022 Mar 29;22(9):8869-80.
25. Truong TP, Nguyen DT, Truong PV. Design and deployment of an IoT-based air quality monitoring system. International Journal of Environmental Science and Development. 2021 May;12(5):139-45.
26. Suriano D. A portable air quality monitoring unit and a modular, flexible tool for on-field evaluation and calibration of low-cost gas sensors. HardwareX. 2021 Apr 1;9:e00198.
27. Siddiqui SA, Fatima N, Ahmad A. Smart air pollution monitoring system with smog prediction model using machine learning. International Journal of Advanced Computer Science and Applications. 2021;12(8).
28. Shams SR, Jahani A, Kalantary S, Moeinaddini M, Khorasani N. Artificial intelligence accuracy assessment in NO2 concentration forecasting of metropolises air. Scientific Reports. 2021 Jan 19;11(1):1805.
29. Feng J, Feng Z, Jiang G, Zhang G, Jin W, Zhu H. A Prediction Method for the Average Winding Temperature of a Transformer Based on the Fully Connected Neural Network. Applied Sciences. 2024 Aug 5;14(15):6841.
30. Sun J, Xia Y. Pretreating and normalizing metabolomics data for statistical analysis. Genes & Diseases. 2024 May 1;11(3):100979.
31. Shin S, Bai L, Burnett RT, Kwong JC, Hystad P, van Donkelaar A, Lavigne E, Weichenthal S, Copes R, Martin RV, Kopp A. Air pollution as a risk factor for incident chronic obstructive pulmonary disease and asthma. A 15-year population-based cohort study. American journal of respiratory and critical care medicine. 2021 May 1;203(9):1138-48.
32. Mumtaz R, Zaidi SM, Shakir MZ, Shafi U, Malik MM, Haque A, Mumtaz S, Zaidi SA. Internet of things (Iot) based indoor air quality sensing and predictive analytic—A COVID-19 perspective. Electronics. 2021 Jan 15;10(2):184.
33. Mujawar TH, Prabhkar P, Chaudhary V, Deshmukh L. Design and development of air quality monitoring system for solapur city using smart technologies: WSN and IoT. Environmental Management: Pollution, Habitat, Ecology, and Sustainability. 2022 Mar 23:3.
34. Likitha NR, Nagalakshmi JT. Improving Prediction Accuracy in Drift Detection Using Random Forest in Comparing with Modified Light Gradient Boost Model. In2024 Ninth International Conference on Science Technology Engineering and Mathematics (ICONSTEM) 2024 Apr 4 (pp. 1-4). IEEE.
35. Sun Z, Wang G, Li P, Wang H, Zhang M, Liang X. An improved random forest based on the classification accuracy and correlation measurement of decision trees. Expert Systems with Applications. 2024 Mar 1;237:121549.
36. Liu L, Liu C, Chen R, Zhou Y, Meng X, Hong J, Cao L, Lu Y, Dong X, Xia M, Ding B. Associations of short-term exposure to air pollution and emergency department visits for pediatric asthma in Shanghai, China. Chemosphere. 2021 Jan 1;263:127856.
37. Bekkar A, Hssina B, Douzi S, Douzi K. Air-pollution prediction in smart city, deep learning approach. Journal of big Data. 2021 Dec;8:1-21.
38. Ionascu ME, Castell N, Boncalo O, Schneider P, Darie M, Marcu M. Calibration of CO, NO2, and O3 using Airify: A low-cost sensor cluster for air quality monitoring. Sensors. 2021 Nov 29;21(23):7977.
39. Goh CC, Kamarudin LM, Zakaria A, Nishizaki H, Ramli N, Mao X, Syed Zakaria SM, Kanagaraj E, Abdull Sukor AS, Elham MF. Real-time in-vehicle air quality monitoring system using machine learning prediction algorithm. Sensors. 2021 Jul 21;21(15):4956.
40. Bano S, Anand V, Kalbande R, Beig G, Rathore DS. Spatio-temporal variability and possible source identification of criteria pollutants from Ahmedabad-a megacity of Western India. Journal of Atmospheric Chemistry. 2024 Dec;81(1):1.
41. Zeinalnezhad M, Chofreh AG, Goni FA, Klemeš JJ. Air pollution prediction using semi-experimental regression model and Adaptive Neuro-Fuzzy Inference System. Journal of Cleaner Production. 2020 Jul 10;261:121218.
2. Lopes D, Ferreira J, Rafael S, Hoi KI, Li X, Liu Y, Yuen KV, Mok KM, Miranda AI. High-resolution multi-scale air pollution system: Evaluation of modelling performance and emission control strategies. Journal of Environmental Sciences. 2024 Mar 1;137:65-81.
3. Varghese D, Ferris K, Lee B, Grigg J, Pinnock H, Cunningham S. Outdoor air pollution and near‐fatal/fatal asthma attacks in children: A systematic review. Pediatric Pulmonology. 2024 May;59(5):1196-206.
4. Brook RD, Rajagopalan S, Al-Kindi S. Public health relevance of US EPA air quality index activity recommendations. JAMA Network Open. 2024 Apr 1;7(4):e245292.
5. Ścibor M, Balcerzak B, Galbarczyk A, Jasienska G. Associations between daily ambient air pollution and pulmonary function, asthma symptom occurrence, and quick-relief inhaler use among asthma patients. International Journal of Environmental Research and Public Health. 2022 Apr 16;19(8):4852.
6. Castro M, Zavod M, Rutgersson A, Jörntén-Karlsson M, Dutta B, Hagger L. iPREDICT: Characterization of Asthma Triggers and Selection of Digital Technology to Predict Changes in Disease Control. Journal of Asthma and Allergy. 2024 Dec 31:653-66.
7. Woo J, Lee JH, Kim Y, Rudasingwa G, Lim DH, Kim S. Forecasting the effects of real-time indoor PM2. 5 on peak expiratory flow rates (PEFR) of asthmatic children in Korea: a deep learning approach. IEEE Access. 2022 Feb 3;10:19391-400.
8. Kothandaraman D, Praveena N, Varadarajkumar K, Madhav Rao B, Dhabliya D, Satla S, Abera W. Intelligent forecasting of air quality and pollution prediction using machine learning. Adsorption Science & Technology. 2022 Jun 27;2022:5086622.
9. Veiga T, Munch-Ellingsen A, Papastergiopoulos C, Tzovaras D, Kalamaras I, Bach K, Votis K, Akselsen S. From a low-cost air quality sensor network to decision support services: Steps towards data calibration and service development. Sensors. 2021 May 5;21(9):3190.
10. Anan SR, Hossain MA, Milky MZ, Khan MM, Masud M, Aljahdali S. [Retracted] Research and Development of an IoT‐Based Remote Asthma Patient Monitoring System. Journal of Healthcare Engineering. 2021;2021(1):2192913.
11. Ra HK, Salekin A, Yoon HJ, Kim J, Nirjon S, Stone DJ, Kim S, Lee JM, Son SH, Stankovic JA. Asthmaguide: an asthma monitoring and advice ecosystem. In2016 IEEE Wireless Health (WH) 2016 Oct 25 (pp. 1-8). IEEE.
12. Khatri KL, Tamil LS. Early detection of peak demand days of chronic respiratory diseases emergency department visits using artificial neural networks. IEEE journal of biomedical and health informatics. 2017 Apr 26;22(1):285-90.
13. Jaimini U, Banerjee T, Romine W, Thirunarayan K, Sheth A, Kalra M. Investigation of an indoor air quality sensor for asthma management in children. IEEE sensors letters. 2017 Apr 6;1(2):1-4.
14. Hosseini A, Buonocore CM, Hashemzadeh S, Hojaiji H, Kalantarian H, Sideris C, Bui AA, King CE, Sarrafzadeh M. HIPAA compliant wireless sensing smartwatch application for the self-management of pediatric asthma. In2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN) 2016 Jun 14 (pp. 49-54). IEEE.
15. Kaffash-Charandabi N, Alesheikh AA, Sharif M. A ubiquitous asthma monitoring framework based on ambient air pollutants and individuals’ contexts. Environmental Science and Pollution Research. 2019 Mar 20;26(8):7525-39.
16. Liu Y, Pan J, Zhang H, Shi C, Li G, Peng Z, Ma J, Zhou Y, Zhang L. Short-term exposure to ambient air pollution and asthma mortality. American journal of respiratory and critical care medicine. 2019 Jul 1;200(1):24-32.
17. Ding L, Zhu D, Peng D, Zhao Y. Air pollution and asthma attacks in children: A case–crossover analysis in the city of Chongqing, China. Environmental pollution. 2017 Jan 1;220:348-53.
18. Shin SW, Bae DJ, Park CS, Lee JU, Kim RH, Kim SR, Chang HS, Park JS. Effects of air pollution on moderate and severe asthma exacerbations. Journal of Asthma. 2020 Aug 2;57(8):875-85.
19. Han CH, Pak H, Chung JH. Short-term effects of exposure to particulate matter and air pollution on hospital admissions for asthma and chronic obstructive pulmonary disease in Gyeonggi-do, South Korea, 2007–2018. Journal of Environmental Health Science and Engineering. 2021 Dec;19:1535-41.
20. Martínez-Rivera C, Garcia-Olivé I, Stojanovic Z, Radua J, Manzano JR, Abad-Capa J. Association between air pollution and asthma exacerbations in Badalona, Barcelona (Spain), 2008–2016. Medicina Clínica (English Edition). 2019 May 3;152(9):333-8.
21. Sharma AK, Saini S, Chhabra P, Chhabra SK, Ghosh C, Baliyan P. Air pollution and weather as the determinants of acute attacks of asthma: Spatiotemporal approach. Indian Journal of Public Health. 2020 Apr 1;64(2):124-9.
22. P Kortoçi P, Motlagh NH, Zaidan MA, Fung PL, Varjonen S, Rebeiro-Hargrave A, Niemi JV, Nurmi P, Hussein T, Petäjä T, Kulmala M. Air pollution exposure monitoring using portable low-cost air quality sensors. Smart health. 2022 Mar 1;23:100241.
23. Anastasiou E, Vilcassim MR, Adragna J, Gill E, Tovar A, Thorpe LE, Gordon T. Feasibility of low-cost particle sensor types in long-term indoor air pollution health studies after repeated calibration, 2019–2021. Scientific reports. 2022 Aug 26;12(1):14571.
24. Singh VK, Singh C, Raza H. Event classification and intensity discrimination for forest fire inference with IoT. IEEE Sensors Journal. 2022 Mar 29;22(9):8869-80.
25. Truong TP, Nguyen DT, Truong PV. Design and deployment of an IoT-based air quality monitoring system. International Journal of Environmental Science and Development. 2021 May;12(5):139-45.
26. Suriano D. A portable air quality monitoring unit and a modular, flexible tool for on-field evaluation and calibration of low-cost gas sensors. HardwareX. 2021 Apr 1;9:e00198.
27. Siddiqui SA, Fatima N, Ahmad A. Smart air pollution monitoring system with smog prediction model using machine learning. International Journal of Advanced Computer Science and Applications. 2021;12(8).
28. Shams SR, Jahani A, Kalantary S, Moeinaddini M, Khorasani N. Artificial intelligence accuracy assessment in NO2 concentration forecasting of metropolises air. Scientific Reports. 2021 Jan 19;11(1):1805.
29. Feng J, Feng Z, Jiang G, Zhang G, Jin W, Zhu H. A Prediction Method for the Average Winding Temperature of a Transformer Based on the Fully Connected Neural Network. Applied Sciences. 2024 Aug 5;14(15):6841.
30. Sun J, Xia Y. Pretreating and normalizing metabolomics data for statistical analysis. Genes & Diseases. 2024 May 1;11(3):100979.
31. Shin S, Bai L, Burnett RT, Kwong JC, Hystad P, van Donkelaar A, Lavigne E, Weichenthal S, Copes R, Martin RV, Kopp A. Air pollution as a risk factor for incident chronic obstructive pulmonary disease and asthma. A 15-year population-based cohort study. American journal of respiratory and critical care medicine. 2021 May 1;203(9):1138-48.
32. Mumtaz R, Zaidi SM, Shakir MZ, Shafi U, Malik MM, Haque A, Mumtaz S, Zaidi SA. Internet of things (Iot) based indoor air quality sensing and predictive analytic—A COVID-19 perspective. Electronics. 2021 Jan 15;10(2):184.
33. Mujawar TH, Prabhkar P, Chaudhary V, Deshmukh L. Design and development of air quality monitoring system for solapur city using smart technologies: WSN and IoT. Environmental Management: Pollution, Habitat, Ecology, and Sustainability. 2022 Mar 23:3.
34. Likitha NR, Nagalakshmi JT. Improving Prediction Accuracy in Drift Detection Using Random Forest in Comparing with Modified Light Gradient Boost Model. In2024 Ninth International Conference on Science Technology Engineering and Mathematics (ICONSTEM) 2024 Apr 4 (pp. 1-4). IEEE.
35. Sun Z, Wang G, Li P, Wang H, Zhang M, Liang X. An improved random forest based on the classification accuracy and correlation measurement of decision trees. Expert Systems with Applications. 2024 Mar 1;237:121549.
36. Liu L, Liu C, Chen R, Zhou Y, Meng X, Hong J, Cao L, Lu Y, Dong X, Xia M, Ding B. Associations of short-term exposure to air pollution and emergency department visits for pediatric asthma in Shanghai, China. Chemosphere. 2021 Jan 1;263:127856.
37. Bekkar A, Hssina B, Douzi S, Douzi K. Air-pollution prediction in smart city, deep learning approach. Journal of big Data. 2021 Dec;8:1-21.
38. Ionascu ME, Castell N, Boncalo O, Schneider P, Darie M, Marcu M. Calibration of CO, NO2, and O3 using Airify: A low-cost sensor cluster for air quality monitoring. Sensors. 2021 Nov 29;21(23):7977.
39. Goh CC, Kamarudin LM, Zakaria A, Nishizaki H, Ramli N, Mao X, Syed Zakaria SM, Kanagaraj E, Abdull Sukor AS, Elham MF. Real-time in-vehicle air quality monitoring system using machine learning prediction algorithm. Sensors. 2021 Jul 21;21(15):4956.
40. Bano S, Anand V, Kalbande R, Beig G, Rathore DS. Spatio-temporal variability and possible source identification of criteria pollutants from Ahmedabad-a megacity of Western India. Journal of Atmospheric Chemistry. 2024 Dec;81(1):1.
41. Zeinalnezhad M, Chofreh AG, Goni FA, Klemeš JJ. Air pollution prediction using semi-experimental regression model and Adaptive Neuro-Fuzzy Inference System. Journal of Cleaner Production. 2020 Jul 10;261:121218.
| Files | ||
| Issue | Vol 10 No 1 (2025): Winter 2025 | |
| Section | Original Research | |
| Keywords | ||
| Air pollution; Artificial intelligent; Asthma attack; Internet of things (IoT); Random forest regression (RFR) | ||
| Rights and permissions | |
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
How to Cite
1.
Fakhrulddin SS, Bhatt V, Gharghan SK. Intelligent air pollution prediction algorithm-based optimized random forest regression for reducing asthmatic attacks. JAPH. 2025;10(1):1-18.
