Abstract
Chronic obstructive pulmonary disease (COPD) affects the health of millions of people worldwide. In this regard, this research tried to present a concept showing that optimized ML models can predict COPD based on the Exasen dataset. Further, the stochastic gamma process was explained as a continuous-time model with gamma-distributed increments and the compound Poisson process for modeling random jumps in Poisson events because of their relevance to modeling irregular patterns. The two algorithms that will be used in this study are extreme Gradient Boosting Classification, or XGBC, and CAT Boost Classification, CAT, both enhanced by the Artificial Rabbit Optimizer, or ARO, for hyperparameter tuning. The performances were measured concerning accuracy, Precision, Recall, and F1 score for COPD prediction. Surprisingly, both optimized models carried out excellent performance in COPD prediction; especially, the XGAR reached more than 0.910 accuracy in the training phase. Each model has a characteristic. Though XGBC yielded slightly high accuracy, the computational resource can be huge. In comparison, CAT had competitive results with XGBC, maybe with faster training times. The results here suggest that optimized XGBC and CAT are promising for COPD prediction on the Exasen dataset. Further studies will be required to confirm these results, especially for clinical applicability and generalizability across various populations. The contribution of this study involves the new application of ARO for the hyperparameter tuning of COPD prediction models with significant enhancements in the accuracies and performances of both XGBC and CAT algorithms on the Exasen dataset. ARO provides enhanced predictive capability by optimizing the critical model parameters and hence has the potential to enhance the effectiveness of ML in medical diagnostics. This work underlines the prospect of ML models with advanced optimization techniques for the betterment of COPD diagnosis, hence helping in its management and personalized treatment.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
The authors do not have permission to share data.
References
Agustí A, Bartolome RC, Gerard JC, David H, Antonio A, Peter B, Jean B, MeiLan KH, Fernando JM, Maria MO (2023) Global initiative for chronic obstructive lung disease 2023 report: GOLD executive summary. Am J Respir Crit Care Med 207(7):819–837
Balasubramaniam S, Sumina S, Kumar KS, Prasanth A (2024) Machine learning based models for implementing digital twins in healthcare industry. In: Arai K (ed) Metaverse technologies in healthcare. Elsevier, pp 135–162
Celli BR, Fabbri LM, Aaron SD, Agusti A, Brook R, Criner GJ, Franssen FME, Humbert M, Hurst JR, O‘Donnell D (2021) An updated definition and severity classification of chronic obstructive pulmonary disease exacerbations: the Rome proposal. Am J Respir Crit Care Med 204(11):1251–1258
Chen T, Guestrin C (2016) Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd international conference on knowledge discovery and data mining. pp 785–94
Chen Y, Yonglin Y, Dongmei Y, Wenbo Z, Vasileios K, Xiaoju C (2024) Developing and validating machine learning-based prediction models for frailty occurrence in those with chronic obstructive pulmonary disease. J Thorac Dis 16(4):2482
Dally EC, Rekha BB (2024) Automated chronic obstructive pulmonary disease (COPD) detection and classification using mayfly optimization with deep belief network model. Biomed Signal Process Control 96:106488
Effros RM, Peterson B, Casaburi R, Su J, Dunning M, Torday J, Biller J, Shaker R (2005) Epithelial lining fluid solute concentrations in chronic obstructive lung disease patients and normal subjects. J Appl Physiol 99(4):1286–1292
Fischer AM, Varga-Szemes A, Martin SS, Sperl JI, Sahbaee P, Neumann D, Gawlitza J, Henzler T, Johnson CM, JW Nance (2020) Artificial intelligence-based fully automated per lobe segmentation and emphysema-quantification based on chest computed tomography compared with global initiative for chronic obstructive lung disease severity of smokers. J Thorac Imaging 35:S28–34
Glyde HMG, Morgan C, Wilkinson TMA, Nabney IT, Dodd JW (2024) Remote patient monitoring and machine learning in acute exacerbations of chronic obstructive pulmonary disease: dual systematic literature review and narrative synthesis. J Med Internet Res 26:e52143
Gorse GJ, O’Connor TZ, Hall SL, Vitale JN, Nichol KL (2009) Human coronavirus and acute respiratory illness in older adults with chronic obstructive pulmonary disease. J Infect Dis 199(6):847–857
Haroon S, Jordan R, Takwoingi Y, Adab P (2015) Diagnostic accuracy of screening tests for COPD: a systematic review and meta-analysis. BMJ Open 5(10):1–10
Himelhoch S, Lehman A, Kreyenbuhl J, Daumit G, Brown C, L Dixon (2004) Prevalence of chronic obstructive pulmonary disease among those with serious mental illness. Am J Psychiatry 161(12):2317–2319
Hnizdo E, Glindmeyer HW, Petsonk EL, Enright P, Buist AS (2006) Case definitions for chronic obstructive pulmonary disease. COPD J Chronic Obstr Pulm Dis 3(2):95–100
Hogg JC, Chu F, Utokaparch S, Woods R, Elliott WM, Buzatu L, Cherniack RM, Rogers RM, Sciurba FC, Coxson HO (2004) The nature of small-airway obstruction in chronic obstructive pulmonary disease. N Engl J Med 350(26):2645–2653
Hoth KF, Wamboldt FS, Bowler R, Make B, KE Holm (2011) Attributions about cause of illness in chronic obstructive pulmonary disease. J Psychosom Res 70(5):465–472
Jayanthi G, Archana E, Saravanan R, Swaminathan A, Sai CN (2024) Comparative analysis of psychological stress detection: a study of artificial neural networks and Cat boost algorithm. Int J Intell Syst Appl Eng 12(1):385–394
Karadag F, Ozcan H, Karul AB, Yilmaz M, Cildag O (2007) Correlates of non-thyroidal illness syndrome in chronic obstructive pulmonary disease. Respir Med 101(7):1439–1446
Li Y, Wang X, Qiao Y, Ren J, Ren H, Cui Y, Liu J, Zhao R, Qiu L (2023) Performance comparison of improved machine learning algorithms based on Bayesian optimization in high-dimensional and unbalanced COPD data. https://doi.org/10.21203/rs.3.rs-3239086/v1
MacLeod M, Papi A, Contoli M, Beghé B, Celli BR, Wedzicha JA, Fabbri LM (2021) Chronic obstructive pulmonary disease exacerbation fundamentals: diagnosis, treatment, prevention and disease impact. Respirology 26(6):532–551
Makimoto K, Au R, Moslemi A, Hogg JC, Bourbeau J, Tan WC, M Kirby (2023) Comparison of feature selection methods and machine learning classifiers for predicting chronic obstructive pulmonary disease using texture-based CT lung radiomic features. Acad Radiol 30(5):900–910
Maleki-Yazdi MR, Kelly SM, Lam SS, Marin M, Barbeau M, V Walker (2012) The burden of illness in patients with moderate to severe chronic obstructive pulmonary disease in Canada. Can Respir J 19:319–324
Mannino DM, Watt G, Hole D, Gillis C, Hart C, McConnachie A, Davey Smith G, Upton M, Hawthorne V, Sin DD (2006) The natural history of chronic obstructive pulmonary disease. Eur Respir J 27(3):627–643
O’Neill ES (2002) Illness representations and coping of women with chronic obstructive pulmonary disease: a pilot study. Heart Lung 31(4):295–302
Peng J, Liu X, Cai Z, Huang Y, Lin J, Zhou M, Xiao Z, Lai H, Cao Z, H Peng (2024) Practice of distributed machine learning in clinical modeling for chronic obstructive pulmonary disease. Heliyon 10:13
Prasanth A, Lakshmi D, Dhanaraj RK, Balusamy B, Sherimon PC (2024) Technological advancement in internet of medical things and blockchain for personalized healthcare: applications and use cases. CRC, Boca Raton
Riad AJ, Hasanien HM, Turky RA, Yakout AH (2023) Identifying the PEM fuel cell parameters using artificial rabbits optimization algorithm. Sustainability 15(5):4625
Rycroft CE, Heyes A, Lanza L, Becker K (2012) Epidemiology of chronic obstructive pulmonary disease: a literature review. Int J Chronic Obstr Pulm Dis 7:457–494
Scharloo M, Kaptein AA, Schlösser M, Pouwels H, Bel EH, Rabe KF, EFM Wouters (2007) Illness perceptions and quality of life in patients with chronic obstructive pulmonary disease. J Asthma 44(7):575–581
Subramanian R, Aruchamy P (2024) An effective speech emotion recognition model for multi-regional languages using threshold-based feature selection algorithm. Circuits Syst Signal Process 43(4):2477–2506
Wang Y (2022) Personality type prediction using decision tree, Gbdt, and Cat Boost. In: 2022 international conference on big data, information and computer network (BDICN). IEEE, pp 552–558
Wang L, Cao Q, Zhang Z, Mirjalili S, Zhao W (2022) Artificial rabbits optimization: a new bio-inspired meta-heuristic algorithm for solving engineering optimization problems. Eng Appl Artif Intell 114:105082
Yin H, Wang K, Yang R, Tan Y, Li Q, Zhu W, Sung S (2024) A machine learning model for predicting acute exacerbation of in-home chronic obstructive pulmonary disease patients. Comput Methods Prog Biomed 246:108005
Yu L, Ruan X, Huang W, Huang N, Zeng J, He J, He R, K Yang (2024) Machine learning-based prediction of in-hospital mortality in patients with pneumonic chronic obstructive pulmonary disease exacerbations. J Asthma 61(3):212–221
Zhang B, Wang J, Chen J, Ling Z, Ren Y, Xiong D, L Guo (2023) Machine learning in chronic obstructive pulmonary disease. Chin Med J 136(05):536–538
Zhu Z, Zhao S, Li J, Wang Y, Xu L, Jia Y, Li Z, Li W, Chen G, Wu X (2024) Development and application of a deep learning-based comprehensive early diagnostic model for chronic obstructive pulmonary disease. Respir Res 25(1):167
Acknowledgements
This work was supported by the National Natural Science Foundation of China (Grant number 81960804) and the Guangxi Natural Science Foundation project (Grant number 2023GXNSFAA026096).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no competing interests.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Feng, Y., Jiang, F., Lu, D. et al. Predicting chronic obstructive pulmonary disease (COPD) with optimized machine learning via leveraging comparative analysis of XGBoost and catboost. J Ambient Intell Human Comput 16, 613–627 (2025). https://doi.org/10.1007/s12652-025-04967-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-025-04967-3