Machine Learning System for Asthma Severity Detection Based on Support Vector Machine
Abstract
Detecting the severity of contagious respiratory illnesses early is essential for effective treatment and healthcare planning. This study explores how machine learning, specifically Support Vector Machine (SVM) and Random Forest (RF), can help classify and predict illness severity based on patient symptoms and clinical data. The system is developed using Python programming language on streamlit environment. The aim of this research is to classify the severity level of Asthma for informed decision and patient care. Random Forest regression and SVM models were successfully developed and trained, and its performance was evaluated using metrics such as Mean Squared Error (MSE) and R². The findings showed an MSE of 0.0173 and an R² of 0.6630. These results indicated that the model could predict the severity index of Asthma in a patient with a good degree of accuracy, capturing approximately 88.3% of the variance in the target variable. Our findings show that while both algorithms perform well, Random Forest provides more accurate predictions due to its ability to capture complex patterns in medical data.
Keywords
Asthma
Severity
Respiratory Disease
Health
Machine Learning
References
Ekong, A. & Udo, E. & Ekong, O. & Inyang, S. (2023). Machine Learning based Model for
the Prediction of Fasting Blood Sugar Level towards Cardiovascular Disease Control
for the Enhancement of Public Health. International Journal of Computer Applications.
5-12. 10.5120/ijca2023922622.
Robinson, L., & Kumar, T. (2023). Pneumonia and child mortality: Challenges in LMICs.
Journal of Computer Sciences, 49(1), 67–79.
Smith, J., & Johnson, T. (2023). Seasonal epidemiology of Asthma and other respiratory
diseases. Journal of Computer Sciences, 49(2), 220–233.
Clark, A., & Kim, J. (2023). Environmental determinants of respiratory illness severity: A
computational approach. Journal of Computational Medicine, 12(3), 315–329.
Gupta, R., Kumar, S., & Patel, H. (2023). Global disparities in respiratory health outcomes.
International Journal of Digital Health, 8(2), 140–157.
Huang, X., Martinez, T., & Cheng, L. (2022). Genetic markers and their role in respiratory
disease progression. Computing in Biology and Medicine, 145, 105456.
Lee, D., & Harris, P. (2023). Addressing antimicrobial resistance in respiratory infections.
Journal of Bioinformatics and Medical Engineering, 17(4), 489–502.
Nguyen, T., & Patel, S. (2024). Seasonal trends in respiratory disease severity. Journal of
Environmental Informatics, 13(1), 101–115.
Park, H., Zhao, L., & Zhang, M. (2023). The role of machine learning in predicting seasonal
respiratory outbreaks. AI in Public Health, 6(2), 240–260.
Smith, A., Robinson, L., & Kumar, P. (2024). Emerging technologies in respiratory disease
modeling. Advances in Medical Computing, 19(1), 55–70.
Zhao, X., & Zhang, W. (2024). Viral load as a predictor of severe respiratory outcomes. Journal
of Computational Epidemiology, 15(3), 210–225.
Albrecht, S., Broderick, D., Dost, K. et al. Forecasting severe respiratory disease
hospitalizations using machine learning algorithms. BMC Med Inform Decis Mak 24,
293 (2024).
Algarni, A. (2024). Smart detection: using supervised machine learning for respiratory
diseases. Advances and Applications in Statistics, 91(12), 1607–1625.
Patil, P., Narawade, V. RESP dataset construction with multiclass classification in respiratory
disease infection detection using machine learning approach. Int. j. inf. tecnol. (2024).
Kassaw, A., Bekele, G., Kassaw, A.K. et al. Prediction of acute respiratory infections using
machine learning techniques in Amhara Region, Ethiopia. Sci Rep 14, 27968 (2024).
Huang C, Ha X, Cui Y and Zhang H (2024) A study of machine learning to predict NRDS
severity based on lung ultrasound score and clinical indicators. Front. Med.
11:1481830. doi: 10.3389/fmed.2024.1481830
Kassaw, A., Bekele, G., Kassaw, A.K. et al. Prediction of acute respiratory infections using
machine learning techniques in Amhara Region, Ethiopia. Sci Rep 14, 27968 (2024).
Kumar, R., Maheshwari, S., Sharma, A. et al. Ensemble learning-based early detection of
Asthma disease. Multimed Tools Appl 83, 5723–5743 (2024).
Centers for Disease Control and Prevention [CDC]. (2020). How COVID-19 Spreads.
Retrieved from https://www.cdc.gov.
Cohen, J., Tohme, R. A., & Qin, X. (2017). Health effects of urbanization and social structure
on infectious disease transmission. Journal of Infectious Disease Research, 23(3), 102-
Dubé, E., Laberge, C., Guay, M., Bramadat, P., Roy, R., & Bettinger, J. A. (2021). Vaccine
hesitancy: An overview. Human Vaccines & Immunotherapeutics, 9(8), 1763–1773.
Fauci, A. S., Lane, H. C., & Redfield, R. R. (2020). Covid-19 – Navigating the Uncharted.
New England Journal of Medicine, 382(13), 1268–1269.
Grohskopf, L. A., Alyanak, E., Broder, K. R., Blanton, L. H., Fry, A. M., & Jernigan, D. B.
(2019). Prevention and Control of Seasonal Asthma with Vaccines: Recommendations
of the Advisory Committee on Immunization Practices. MMWR Recommendations
and Reports, 68(3), 1-21.
Moriyama, M., Hugentobler, W. J., & Iwasaki, A. (2020). Seasonality of respiratory viral
infections. Annual Review of Virology, 7(1), 83-101.
Omer, S. B., Betsch, C., & Leask, J. (2019). Vaccination hesitancy and health care providers.
Pediatrics, 144(5), e20190345.
Ranney, M. L., Griffeth, V., & Jha, A. K. (2020). Critical supply shortages—The need for
ventilators and personal protective equipment during the COVID-19 pandemic. New
England Journal of Medicine, 382(18), e41.
Salmon, D. A., Dudley, M. Z., Glanz, J. M., & Omer, S. B. (2015). Vaccine hesitancy: Causes,
consequences, and a call to action. American Journal of Preventive Medicine, 49(6),
S391-S398.
World Health Organization [WHO]. (2019). Ten threats to global health in 2019. Retrieved
from https://www.who.int.
Centers for Disease Control and Prevention [CDC]. (2020). How COVID-19 Spreads.
Retrieved from https://www.cdc.gov.
Moriyama, M., Hugentobler, W. J., & Iwasaki, A. (2020). Seasonality of respiratory viral
infections. Annual Review of Virology, 7(1), 83-101.
Ranney, M. L., Griffeth, V., & Jha, A. K. (2020). Critical supply shortages—The need for
ventilators and personal protective equipment during the COVID-19 pandemic. New
England Journal of Medicine, 382(18), e41.
World Health Organization [WHO]. (2021). COVID-19 Pandemic Response. Retrieved from
https://www.who.int.
Ahmed I. Taloba, R.T. Matoog, Detecting respiratory diseases using machine learning-based
pattern recognition on spirometry data, Alexandria Engineering Journal,Volume
113,2025,Pages 44-59,.
Smith, J., & Johnson, T. (2023). Epidemiological trends in respiratory illnesses: The role of
predictive modeling. Journal of Computer Sciences, 49(3), 221–234.
Miller, R., Zhang, H., & Lee, C. (2023). Machine learning in the surveillance of seasonal
Asthma outbreaks. Journal of Computer Sciences, 48(2), 178–190.
Harris, D., Gupta, S., & Robinson, L. (2022). Socioeconomic impacts of contagious respiratory
diseases: A computational approach. Journal of Computer Sciences, 47(5), 334–347.
Nguyen, K., & Park, S. (2023). The integration of health informatics in managing COVID-19
and beyond. Journal of Computer Sciences, 50(1), 11–25.
Robinson, A., & Zhang, M. (2024). Addressing inequities in respiratory illness interventions
through machine learning. Journal of Computer Sciences, 51(4), 402–416.
Gupta, S., & Zhang, M. (2022). Socioeconomic determinants of respiratory illness burden in
LMICs: A computational approach. Journal of Computer Sciences, 48(3), 201–213.
Lee, J., & Patel, K. (2022). Global trends in RSV epidemiology and healthcare implications.
Journal of Computer Sciences, 47(5), 287–299.
Miller, R., Nguyen, K., & Park, S. (2023). COVID-19 and its impact on the epidemiology of
respiratory illnesses. Journal of Computer Sciences, 50(2), 132–145.
Park, H., & Harris, D. (2024). Predictive modeling in respiratory illness surveillance:
Applications and outcomes. Journal of Computer Sciences, 51(4), 405–418.
Ekong, A., Ekong B., and Edet A. (2022). Supervised machine learning model for effective
classification of patients with covid-19 symptoms based on bayesian belief
network. Researchers Journal of Science and Technology, 2(1), 27-33.
Ekong, B., Ekong, O., Silas, A., Edet, A. E., & William, B. (2023). Machine Learning
Approach for Classification of Sickle Cell Anemia in Teenagers Based on Bayesian
Network. Journal of Information Systems and Informatics, 5(4), 1793-1808.
Edet, A. E., & Ansa, G. O. (2023). Machine learning enabled system for intelligent
classification of host-based intrusion severity. Global Journal of Engineering and
Technology Advances, 16(03), 041-050.
Ebong, O., Edet, A., Uwah, A., & Udoetor, N. (2024). Comprehensive Impact Assessment of
Intrusion Detection and Mitigation Strategies Using Support Vector Machine
Classification.
Mbaji, I.N. & Ebirim, P.U. (2015). Quality assurance in teacher education: inh
