Asthma Disease Prediction Using Regression Tree Method

Yustisia Lisa Christi; Genrawan Hoendarto; Jimmy Tjen

doi:10.18535/sshj.v9i01.1443

Abstract

Asthma is a common, chronic inflammatory disorder of the airways that affects an estimated 339 million people worldwide. Diagnostic approaches for asthma usually fail in clinical practice, partly owing to the multifactorial spectrum of the disease. This study reveals a new diagnostic algorithm where regression trees along with entropy-based subset selection(E-SS) are combined for more reliable and accurate asthma diagnosis. E-SS helps to filter out the most important features from high-dimensional datasets and avoids possible overfitting of the rate up to 91.304%, which is higher than other algorithms like Bayesian Network of 83.3%. The strength of this model is that it can capture complex (non-linear) interactions efficiently between the variables and therefore would be efficient, in particular for asthma prediction. Moreover, it is a more patient-centered methodology where risk factors of each individual are targeted. The model could aid in the diagnosis and treatment of other chronic diseases outside asthma, further alleviating global health care systems.

Keywords

Family PlanningTotal Fertility RateStakeholdersStable PopulationKrama Bali Family Planning

References

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[refR-3] S. A, M, P and R. J, Brown, “Pathogenesis of Asthma: An Overview,” Allergy Clin. Immunol. Rev., vol. 58, no. 3, pp. 150–165, 2022.Google Scholar ↗

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[refR-5] J. L, Ramirez and C. E, Thompson, “Economic Burden of Asthma: A Comprehensive Review,” Health Econ. Rev., vol. 13, no. 1, pp. 75–85, 2023.Google Scholar ↗

[refR-6] L. G, Lee, “Regression Tree Analysis: Techniques and Applications,” Stat. Methods Med. Res., vol. 30, no. 1, pp. 12–25, 2022.Google Scholar ↗

[refR-7] E. K, White and M. S, Zhang, “Using Regression Trees to Understand Asthma Risk Factors,” J. Clin. Epidemology, vol. 89, pp. 45–55, 2024.Google Scholar ↗

[refR-8] T. C, Miller, “Identifying Asthma Phenotypes with Regression Trees,” Am. J. Respir. Crit. Care Med., vol. 207, no. 4, pp. 484–493, 2023.Google Scholar ↗

[refR-9] B. N, Kim, “Personalized Treatment Plans for Asthma: The Role of Regression Trees,” Eur. Respir. Rev., vol. 32, no. 167, pp. 220–230, 2023.Google Scholar ↗

[refR-10] R. J, Allen and M. P, Davis, “Biomarkers and Environmental Triggers of Severe Asthma,” Clin. Immunol., vol. 208, pp. 13–21, 2022.Google Scholar ↗

[refR-11] H. J, Stevens, “Handling High-Dimensional Data in Asthma Research,” J. Comput. Biol., vol. 30, no. 5, pp. 789–800, 2024.Google Scholar ↗

[refR-12] K. E, Robinson, “Integrating Genetic and Environmental Data in Asthma,” Nat. Rev. Genet., vol. 25, no. 8, pp. 532–546, 2023.Google Scholar ↗

[refR-13] P. J, Patel and R. T, Kumar, “Advancements in Asthma Research with Regression Trees,” Med. Data Anal., vol. 14, no. 2, pp. 102–115, 2024.Google Scholar ↗

[refR-14] L. H, Zhang, “Machine Learning in Healthcare: Applications to Asthma,” J. Biomed. Inform., vol. 112, pp. 103–116, 2024.Google Scholar ↗

[refR-15] N. P, Clark, “Transforming Asthma Management with Personalized Approach,” Lancet Respir. Med., vol. 12, no. 7, pp. 678–690, 2023.Google Scholar ↗

[refR-16] Y. Zhang and X. Zhao, “A comprehensive review of ensmble-based feature selection methods,” J. Mach. Learn., vol. 20, no. 1, pp. 1–25, 2019.Google Scholar ↗

[refR-17] F. Smarra and A. D. Innocenzo, “Learning methods for structural damage detection via entropy-based sensors selection,” no. September 2021, pp. 6035–6067, 2022, doi: 10.1002/rnc.6124.DOI ↗Google Scholar ↗

[refR-18] T. Jimmy, “Identifikasi Parameter Kualitas Bahan Pangan dengan Metode Entropy-Based Subset Selection (E-SS) (Studi Kasus: Minuman Anggur),” J. Teknol. Inf. dan Ilmu Komput., vol. 11, no. 1, pp. 47–54, 2024.Google Scholar ↗

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