Authors : Aakash Bhattacharya; Siddhartha Singh

Volume/Issue : Volume 10 - 2025, Issue 9 - September

Google Scholar : https://tinyurl.com/5n72hbv8

Scribd : https://tinyurl.com/4nrnntyk

DOI : https://doi.org/10.38124/ijisrt/25sep783

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Note : Google Scholar may take 30 to 40 days to display the article.

Abstract : Respiratory illnesses, including Acute Respiratory Distress Syndrome (ARDS), asthma, cystic fibrosis, and Chronic Obstructive Pulmonary Disease (COPD), represent an escalating global health concern, impacting a substantial segment of the population and ranking among the foremost causes of mortality worldwide. A critical aspect of managing these conditions is the occurrence of flare-ups, which mark a sudden and severe aggravation of symptoms like shortness of breath, palpitation, and persistent cough, demanding urgent medical care. These acute episodes are often triggered by airway inflammation (bronchitis), alveolar damage (emphysema), and exposure to various environmental irritants such as dust, smoke, chemicals, and fumes. To address this clinical challenge, a model has been presented that aims to uncover the most influential causative factors behind flare-ups and accurately classify a patient’s risk of experiencing them based on distinct prognostic markers.

Keywords : Acute Respiratory Distress Syndrome (ARDS), Chronic Pulmonary Obstructive Disease (COPD), Ensembling, Genetic Algorithm, Neural Networks, Random Forest.

References :

1. Agarwal, A. K., A. Raja, and B. D. Brown (2023, August). Chronic obstructive pulmonary disease. In StatPearls. Treasure Island, FL: StatPearls Publishing. Updated 2023 Aug 7. https://www.ncbi.nlm.nih.gov/books/NBK559281/  
2. Albert, R. K., J. Connett, W. C. Bailey, R. Casaburi, J. A. Cooper, G. J. Criner, J. L. Curtis, M. T. Dransfield, M. K. Han, S. C. Lazarus, B. Make, N. Marchetti, F. J. Martinez, N. E. Madinger, C. McEvoy, D. E. Niewoehner, J. Porsasz, C. S. Price, J. Reilly, P. D. Scanlon, F. C. Sciurba, S. M. Scharf, G. R. Washko, P. G. Woodruff, and N. R. Anthonisen (2011, August). Azithromycin for prevention of exacerbations of COPD. New England Journal of Medicine 365 (8), 689–698. COPD Clinical Research Network. https://doi.org/10.1056/NEJMoa1104623
3. Beltramo, G., J. Cottenet, A.-S. Mariet, M. Georges, L. Piroth, P. Tubert-Bitter, P. Bonniaud, and C. Quantin (2021). Chronic respiratory diseases are predictors of severe outcome in COVID-19 hospitalised patients: A nationwide study. European Respiratory Journal 58, 2004474. https://doi.org/10.1183/13993003.04474-2020
4. Bhattacharya, A., R. Khatri, and T. Pradhan (2017, September). A proactive intelligent model for identification of reduced factors affecting severity of coronary artery disease and its prediction. In International Conference on Advances in Computing, Communications and Informatics (ICACCI), Udupi, India, pp. 2032–2038. IEEE. https://doi.org/10.1109/ICACCI.2017.8126056
5. Bhattacharya, A., S. Singh, and S. Sinha (2019, 04). Factors affecting conditional flare-ups in patients with respiratory diseases and its classification (unpublished work).
6. Burt, L. and S. Corbridge (2013). Copd exacerbations. American Journal of Nursing 113, 34–43. https://doi.org/10.1097/01.naj.0000426688.96330.60 
7. Chapman, K. R. (2018). Increasing awareness of copd: two steps forward, one step back. Chronic Obstructive Pulmonary Disease 5 (4), 228–230. editorial. https://doi.org/10.15326/jcopdf.5.4.2018.0154
8. Chapman, K. R., D. M. Mannino, J. B. Soriano, P. A. Vermeire, A. S. Buist, M. J. Thun, C. Connell, A. Jemal, T. A. Lee, M. Miravitlles, S. Aldington, and R. Beasley (2006). Epidemiology and costs of chronic obstructive pulmonary disease. European Respiratory Journal 27(1), 188–207. https://doi.org/10.1183/09031936.06.00024505
9. Donaldson, G. C., T. A. R. Seemungal, A. Bhowmik, and J. A. Wedzicha (2002). Relationship between exacerbation frequency and lung function decline in chronic obstructive pulmonary disease. Thorax 57(10), 847–852. https://doi.org/10.1136/thorax.57.10.847
10. Hamid, M., S. Lokman, A. Hicham, and M. Hafedh (2017). Context relevant prediction model for copd domain using bayesian belief network. Sensors 17(7), 1486. https://doi.org/10.3390/s17071486
11. Naim, P., P. H. Wuillemin, P. Leray, O. Pourret, and A. Becker (2007). Reseaux Bayesiens. Paris, France: Eyrolles. [Publication in French]. https://www.researchgate.net/publication/234882987_Reseaux_Bayesiens
12. PasseportSante (2019). Bronchite et emphyseme. Last accessed 15 Jan 2019. [Source in French].
13. Soci ́et ́e Canadienne de Thoracologie (2010). Le fardeau humain et financier de la mpoc. Technical report, Soci ́et ́e Canadienne de Thoracologie, Ottawa, ON, Canada. [Publication in French]. PUBNUM: 221681. OCLC: (OCoLC)758215903. https://librarysearch.mtroyal.ca/permalink/01MTROYAL_INST/o62j6r/alma9923218202704656
14. Uzun, S., R. S. Djamin, J. A. J. W. Kluytmans, P. G. H. Mulder, N. E. van’t Veer, A. A. M. Ermens, A. J. Pelle, H. C. Hoogsteden, J. G. J. V. Aerts, and M. M. van der Eerden (2014, May). Azithromycin maintenance treatment in patients with frequent exacerbations of chronic obstructive pulmonary disease (COLUMBUS): a randomised, double-blind, placebo-controlled trial. Lancet Respiratory Medicine 2(5), 361–368. https://doi.org/10.1016/s2213-2600(14)70019-0 
15. World Health Organization (2014). Chronic Respiratory Diseases. Accessed: 05-Oct-2018. www.who.int/gard/publications/chronic_respiratory_diseases.pdf
16. World Health Organization (2014). Chronic Respiratory Diseases - Burden of COPD. Accessed: 05-Oct-2018. www.who.int/respiratory/copd/burden/en/index.html
17. World Health Organization (2018). Global Tuberculosis Report. Accessed: 11-Jun-2025. https://www.who.int/publications/i/item/9789241565646
18. World Health Organization (2024). Chronic Obstructive Pulmonary Disease (COPD). Fact sheet. Updated 2023. Accessed 20-May-2025. https://www.who.int/news-room/fact-sheets/detail/chronic-obstructive-pulmonary-disease-(copd)
19. Centers for Disease Control and Prevention (2018). Asthma: Key Facts. Accessed: 20-May-2025. Xu, J., S. L. Murphy, K. D. Kochanek, B. Bastian, and E. Arias (2018, July). Deaths:  Final data for 2016. National Vital Statistics Reports 67 (5), 1–76. https://www.cdc.gov/nchs/data/nvsr/nvsr67/nvsr67_05.pdf 
20. Yousuf, A. J., G. Parekh, M. Farrow, G. Ball, S. Graziadio, K. Wilson, C. Lendrem, L. Carr, L. Watson, S. Parker, J. Finch, S. Glover, V. Mistry, K. Porter, A. Duvoix, L. O’Brien, S. Rees, K. E. Lewis, P. Davis, and C. E. Brightling (2024). Artificial neural network risk prediction of chronic obstructive pulmonary disease (COPD) exacerbations using urine biomarkers. ERJ Open Research Early Access, 00797–2024. Published Online: January 31, 2024. https://doi.org/10.1183/23120541.00797-2024