Identification and Modelling Tuberculosis Incidence Risk Factors in West Java with Negative Binomial Mixed Model Random Forest

Restu Arisanti, Resa Septiani Pontoh, Sri Winarni, Nisa Akbarilah Putri, Stefany Maurin

Abstract


Tuberculosis (TB) remains a major public health problem in many parts of the world, including in West Java Province, Indonesia. By guiding targeted medication, an accurate assessment of TB risk factors can enhance overall efforts to control tuberculosis. This study introduces modelling by integrating Negative Binomial Mixed Models (NBMM) and Random Forest (RF) called the Negative binomial mixed model random forest (NBMMRF) model.  This model is used to identify and assess risk factors associated with the incidence of tuberculosis. First, utilized NBMM to add fixed effects and random effects in the model and compensate for overdispersion. Modelling count data with overdispersion is a crucial problem in epidemiological studies, and the NBMM component in this model provides a flexible. Afterward, we include a Random Forest component in the model, which helps us detect relevant predictive features and change model weights accordingly. The resulting Negative Binomial Mixed Model Random Forest (NBMMRF) has a high accuracy value of up to 0.915. In contrast to simpler models, the NBMMRF model can capture complex and nonlinear interactions between predictors and outcomes.

Keywords


Tuberculosis; Negative Binomial Mixed Model; Random Forest; NBMMRF

Full Text:

PDF

References


[1] R. Arisanti et al., “Integrating Generalized Linear Mixed Models With Extreme Neural Network: Enhancing Pulmonary Tuberculosis Risk Modeling In West Java, Indonesia,” pp. 1–24, 2024.
[2] S. Sulistyawati and A. W. Ramadhan, “Risk Factors for Tuberculosis in an Urban Setting in Indonesia: A Case-control Study in Umbulharjo I, Yogyakarta,” J. UOEH, vol. 43, no. 2, pp. 165–171, Jun. 2021, doi: 10.7888/juoeh.43.165.
[3] C. E. McCulloch, “Maximum Likelihood Algorithms for Generalized Linear Mixed Models,” J. Am. Stat. Assoc., vol. 92, no. 437, p. 162, Mar. 1997, doi: 10.2307/2291460.
[4] X. Zhang et al., “Negative binomial mixed models for analyzing microbiome count data,” BMC Bioinformatics, vol. 18, no. 1, pp. 1–10, 2017, doi: 10.1186/s12859-016-1441-7.
[5] S. D. Kachman, “An Introduction To Generalized Linear Mixed Models Stephen D . Kachman,” Statistics (Ber)., vol. 24, pp. 59–73, 2008, [Online]. Available: http://armyconference.org/ACAS2003CD/ACAS2003/McCullochCharles/mcculloch.pdf
[6] E. P. Liski, “Generalized Linear Mixed Models: Modern Concepts, Methods and Applications by Walter W. Stroup,” Int. Stat. Rev., vol. 81, no. 3, pp. 482–483, Dec. 2013, doi: 10.1111/insr.12042_24.
[7] B. Charbuty and A. Abdulazeez, “Classification Based on Decision Tree Algorithm for Machine Learning,” J. Appl. Sci. Technol. Trends, vol. 2, no. 01, pp. 20–28, 2021, doi: 10.38094/jastt20165.
[8] Lakshmi Prasanna and S. Mehrotra, “Comparative Analysis of Machine Learning Algorithms on Mental Health Dataset,” Lect. Notes Networks Syst., vol. 719 LNNS, no. 2, pp. 599–606, 2023, doi: 10.1007/978-981-99-3758-5_54.
[9] T. W. Utami, “Analisis Regresi Binomial Negatif Untuk Mengatasi Overdispersion Regresi Poisson Pada Kasus Demam Berdarah Dengue,” J. Stat., vol. 1, no. 2, pp. 59–65, 2013, [Online]. Available: https://jurnal.unimus.ac.id/index.php/statistik/article/view/961
[10] A. A. Yirga, S. F. Melesse, H. G. Mwambi, and D. G. Ayele, “Negative binomial mixed models for analyzing longitudinal CD4 count data,” Sci. Rep., vol. 10, no. 1, p. 16742, Oct. 2020, doi: 10.1038/s41598-020-73883-7.
[11] P. R. Donald, B. J. Marais, and C. E. Barry, “Age and the epidemiology and pathogenesis of tuberculosis,” Lancet, vol. 375, no. 9729, pp. 1852–1854, May 2010, doi: 10.1016/S0140-6736(10)60580-6.




DOI: https://doi.org/10.18860/cauchy.v10i2.29750

Refbacks

  • There are currently no refbacks.


Copyright (c) 2025 Restu Arisanti, Resa Septiani Pontoh, Sri Winarni, Nisa Akbarilah Putri, Stefany Maurin

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Editorial Office
Mathematics Department,
Universitas Islam Negeri Maulana Malik Ibrahim Malang
Gajayana Street 50 Malang, East Java, Indonesia 65144
Faximile (+62) 341 558933
e-mail: cauchy@uin-malang.ac.id

Creative Commons License
CAUCHY: Jurnal Matematika Murni dan Aplikasi is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.