MACHINE LEARNING MODELING FOR FORECASTING FORCE VELOCITY CAPACITY
DOI:
https://doi.org/10.71146/kjmr1030Keywords:
Spirometry analysis, Force Velocity Capacity, machine learning, forecastingAbstract
A spirometer is a device used to monitor people with respiratory diseases who are unable to meet their own needs due to their poor health. Thus, spirometry was used to measure significant parameters. The present study attempted to develop machine-learning models based on Random Forest, KNN regression, Decision Tree Regression, and Support Vector Regression to forecast force velocity capacity values when the rigorous end-of-test conditions were not fully met. Smoking has become the leading cause of reduces lung function among males. We assessed the effects of smoking on lung function. To perform this study, a sample of 120 industrial workers was used. Traditional machine-learning algorithms were used and compared their accuracies and other performance measures. We found that the best algorithm was K-nearest Neighbor with 78 percent Coefficient of determination. These forecasted outcomes play a crucial role in the evaluation of spirometry measures in clinical practice since they serve as reference values. Research advises industrial workers to keep a healthy body and abstain from smoking to survive their respiratory systems.
Downloads
References
Alam, M. Z., Simonetti, A., Brillantino, R., Tayler, N., Grainge, C., Siribaddana, P., ... & Rezwan, F. I. (2022). Predicting pulmonary function from the analysis of voice: a machine learning approach. Frontiers in digital health, 4, 750226.
Alqahtani, J. S. (2022). Prevalence, incidence, morbidity and mortality rates of COPD in Saudi Arabia: Trends in burden of COPD from 1990 to 2019. PLoS One, 17(5), e0268772.
Bolourani, S., Wang, P., Patel, V. M., Manetta, F., & Lee, P. C. (2020). Predicting respiratory failure after pulmonary lobectomy using machine learning techniques. Surgery, 168(4), 743-752.
do Amaral, J. L. M., & de Melo, P. L. (2020). Clinical decision support systems to improve the diagnosis and management of respiratory diseases. In Artificial intelligence in precision health (pp. 359-391). Academic Press.
Erdal, M. (2021). Chronic obstructive pulmonary disease (COPD): exacerbations and costs.
Hashemi, S. Y., Momenabadi, V., Faramarzi, A., & Kiani, A. (2020). Trends in burden of chronic obstructive pulmonary disease in Iran, 1995–2015: findings from the global burden of disease study. Archives of Public Health, 78, 1-7.
Ioachimescu et al. "Estimating FVC from FEV2 and FEV3: Assessment of a surrogate spirometric parameter." Chest128.3(2005):1274-1281
Kavitha et al. "Prediction of forced expiratory volume (FEV6) in flow volume spirometry using support vector machines and radial basis function neural networks." Journal of Mechanics in Medicine & Biology 10.04(2010): 683-693.
Kristensen, K., Olesen, P. H., Roerbaek, A. K., Nielsen, L., Hansen, H. K., Cichosz, S. L., ... & Hejlesen, O. (2023). Using random forest machine learning on data from a large, representative cohort of the general population improves clinical spirometry references. The Clinical Respiratory Journal, 17(8), 819-828.
Licia Pezzoli et al. "Quality of spirometric performance in older people. " Age & Ageing 32.1(2002): 43-46.
Lin, C. H., Cheng, S. L., Chen, C. Z., Chen, C. H., Lin, S. H., & Wang, H. C. (2023). Current progress of COPD Early detection: key points and novel strategies. International Journal of Chronic Obstructive Pulmonary Disease, 1511-1524.
Maldonado-Franco, A., Giraldo-Cadavid, L. F., Tuta-Quintero, E., Bastidas Goyes, A. R., & Botero-Rosas, D. A. (2023). The Challenges of Spirometric Diagnosis of COPD. Canadian Respiratory Journal, 2023(1), 6991493.
Mengesha, Y. A., & Mekonnen, Y. (1985). Spirometric lung function tests in normal non-smoking Ethiopian men and women. Thorax, 40(6), 465-468.
Mythili et al. "Prediction of FEV1 and FEV6 in normal and obstructive abnormality using ELM regression and spirometric investigations." International Conference on Informatics 2015.
Sharma, S. K., Narain, J. P., & Mohan, A. (2023). Chronic Obstructive Lung Diseases. In Global Health Essentials (pp. 163-167). Cham: Springer International Publishing.
Schermer TR et al. "Validity of spirometric testing in a general practice population of patients with chronic obstructive pulmonary disease (COPD)." Thorax 58.10(2003): 861-6.
Wang, C., Chen, X., Zhao, R., He, Z., Zhao, Z., Zhan, Q., ... & Fang, Z. (2019). Predicting forced vital capacity (FVC) using support vector regression (SVR). Physiological measurement, 40(2), 025010.
Yao, L. P., & Tao, R. (2021). Does Chronic Obstructive Pulmonary Disease Affect Workers' Health? Frontiers in Public Health, 9, 711629.
Subramaniyan, V., Fuloria, S., Sekar, M., Shanmugavelu, S., Vijeepallam, K., Kumari, U., ... & Fuloria, N. K. (2023). Introduction to Lung Disease. In Targeting Epigenetics in Inflammatory Lung Diseases (pp. 1-16). Singapore: Springer Nature Singapore.
Singh, A., Prakash, N., & Jain, A. (2023). A review on prevalence of worldwide COPD situation. Proceedings of Data Analytics and Management: ICDAM 2022, 391-405.
Ulmer WT. "Lung function - Clinical importance, problems and new results." Journal of Physiology and Pharmacology, 54.1(2005): 11-13.
Jaakkola, J. J., Hernberg, S., Lajunen, T. K., Sripaijboonkij, P., Malmberg, L. P., & Jaakkola, M. S. (2019). Smoking and lung function among adults with newly onset asthma. BMJ open respiratory research, 6(1), e000377.
Kumar, J., Khuhawar, S. M., Chandio, B. A., Sanghro, K. A., & Channa, S. M. (2022). Effect of Smoking on Chest Expansion, Respiratory and Lung Functions. Pakistan Journal of Medical & Health Sciences, 16(06), 340-340.
Enayatjazi, M., Enayatjazi, S., Ghadiri, N., & Fathi, A. (2023). Effect of 8 weeks of Endurance Training on Volume and Capacity of Lungs (FEV1/FVC, FEV1, FVC) and Its Relationship with BMI in Male Smoking students. New Approaches in Exercise Physiology, 5(9), 32-43.
Khan, F. A., Stewart, I., Moss, S., Fabbri, L., Robinson, K. A., Johnson, S. R., & Jenkins, R. G. (2022). Three-month FVC change: a trial endpoint for idiopathic pulmonary fibrosis based on individual participant data meta-analysis. American journal of respiratory and critical care medicine, 205(8), 936-948.
Miller MR et al. "Standardisation of spirometry." European Respiratory Journal 26.2(2005): 319-338
Tang Y, Zhang M, Feng Y, Liang B. The measurement of lung volumes using body plethysmography and helium dilution methods in COPD patients: a correlation and diagnosis analysis. Sci Rep. (2016) 6:37550. doi: 10.1038/srep37550
O’Donnell CR, Bankier AA, Stiebellehner L, Reilly JJ, Brown R, Loring SH. Comparison of plethysmographic and helium dilution lung volumes: which is best for COPD? Chest. (2010) 137:1108–15. doi: 10.1378/chest.09-1504
Roy, A., Al Zubayer, T., Tabassum, N., Islam, M. N., & Sattar, M. A. (2022). CurFi: An automated tool to find the best regression analysis model using curve fitting. Engineering Reports, 4(12), e12522.
Viering, T., & Loog, M. (2022). The shape of learning curves: a review. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(6), 7799-7819.
Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications and research directions. SN computer science, 2(3), 160.
Kamel, A. R., & Abonazel, M. R. (2023). A simple introduction to regression modeling using R. Computational Journal of Mathematical and Statistical Sciences, 2(1), 52-79.
Hastie, T., Tibshirani, R., Friedman, J., Hastie, T., Tibshirani, R., & Friedman, J. (2009). Random forests. The elements of statistical learning: Data mining, inference, and prediction, 587-604.
Bernard, S., Heutte, L., & Adam, S. (2009, June). On the selection of decision trees in random forests. In 2009 International joint conference on neural networks (pp. 302-307). IEEE.
Zhang, F., & O'Donnell, L. J. (2020). Support vector regression. In Machine learning (pp. 123-140). Academic Press.
Mishra, P. (2021). Explainability for Non-Linear Models. In Practical Explainable AI Using Python: Artificial Intelligence Model Explanations Using Python-based Libraries, Extensions, and Frameworks (pp. 93-127). Berkeley, CA: Apress.
Boswell, D. (2002). Introduction to support vector machines. Departement of Computer Science and Engineering University of California San Diego, 11, 16-17.
Günlük, O., Kalagnanam, J., Li, M., Menickelly, M., & Scheinberg, K. (2021). Optimal decision trees for categorical data via integer programming. Journal of global optimization, 81, 233-260.
Oswald, R. K., Scherer, W. T., & Smith, B. L. (2000). Traffic flow forecasting using approximate nearest neighbor nonparametric regression (No. UVA-CE-ITS_01-4).
Sabry, F. (2023). K Nearest Neighbor Algorithm: Fundamentals and Applications (Vol. 28). One Billion Knowledgeable.
Downloads
Published
License
Copyright (c) 2026 Muhammad Mubashar Ali, Javed Iqbal, Sahar Iqbal, Muhammad Awais (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
KJMR publishes all articles as open access under CC BY 4.0, allowing anyone to share and adapt the work, even commercially, with proper credit, a license link, and clear notice of changes, without implying endorsement. Authors retain copyright while granting the journal non-exclusive publishing and archiving rights and may self-archive without embargo. Third-party material requires proper permission, and the journal ensures long-term free access through its website and archiving partners.
How to Cite
