Integrated model of factor selection and categorical regression for predicting the implantation risk of diagnostic-therapeutic devices in gastroesophageal junction pathology
DOI:
https://doi.org/10.17721/1812-5409.2026/1.25Keywords:
device implantation, factor analysis, categorical regression, risk, gastroesophageal junction, risk stratificationAbstract
The article presents an integrated method for predicting the implantation risk of diagnostic-therapeutic devices in the gastroesophageal junction based on factor analysis and categorical regression. In a sample of 558 patients, 15 clinical and endoscopic indicators were selected, characterizing the mucosal condition, anatomical features, and reflux symptoms. Factor analysis using principal axis factoring with oblimin rotation reduced the initial set of variables to three latent factors: F1 – "clinical and morphological features of gastroesophageal reflux disease", F2 – "anatomical and functional indicators of the esophagus", and F3 – "erosive mucosal lesions". These factors were used as predictors in categorical regression to forecast five levels of implantation risk. The model demonstrated high predictive quality (R2 = 0.858), all factors were statistically significant (p < 0.001) and multicollinearity indicators of predictors > 0.8 confirmed the absence of critical multicollinearity. The resulting model provides standardized risk stratification, enabling clinicians to more objectively assess the appropriateness of device implantation and identify patients who require additional optimization prior to the procedure.
Pages of the article in the issue: 187 - 194
Language of the article: Ukrainian
References
Akoglu, H. (2018). User's guide to correlation coefficients. Turk J Emerg Med, 18(3), 91–93. https://doi.org/10.1016/j.tjem.2018.08.001
Devlieger, I. (2019). Factor score regression: Models for the analysis of variables and factors [Doctoral dissertation, Ghent University]. Ghent University Academic Repository. https://backoffice.biblio.ugent.be/download/8655145/8655146
Finch, W. H., & French, B. F. (2018). Educational and Psychological Measurement (1st ed.). Routledge. https://doi.org/10.4324/9781315650951
Guo, J., Wall, M., & Amemiya, Y. (2006). Latent class regression on latent factors. Biostatistics, 7(1), 145–163. https://doi.org/10.1093/biostatistics/kxi046
IBM. (n. d.). CATREG. IBM SPSS Statistics Algorithms. https://public.dhe.ibm.com/software/analytics/spss/support/Stats/Docs/Statistics/Algorithms/14.0/catreg.pdf
Lawenko, R. M. A., & Lee, Y. Y. (2016). Evaluation of gastroesophageal reflux disease using the Bravo capsule pH system. Journal of Neurogastroenterology and Motility, 22(1), 25–30. https://pubmed.ncbi.nlm.nih.gov/26717929/
Li, J. N., Liu, C. L., & Tao, X. H. (2013). Clinical utility and tolerability of JSPH-1 wireless esophageal pH monitoring system. BMC Gastroenterology, 13(10). https://doi.org/10.1186/1471-230X-13-10
Liao, Y. (2019). Feature Selection: Removing Multicollinearity – Python Automation and Machine Learning for EM and ICs. An Online Book, First Edition. https://www.globalsino.com/ICs/page3268.html?utm_source=chatgpt.com
Smith, L. E., Smith, D. K., Blume, J. D., Siew, E. D., & Billings IV, F. T. (2017). Latent variable modeling improves AKI risk factor identification and AKI prediction compared to traditional methods. BMC Nephrology, 18(1), 55. https://doi.org/10.1186/s12882-017-0465-1
Statistics Solutions. (n. d.). Comprehensive guide to factor analysis. https://www.statisticssolutions.com/free-resources/directory-of-statistical-analyses/factor-analysis/?utm_source=chatgpt.com
Viana, A. T., Gonçalves, K. C. M., & Paez, M. S. (2024). Factor model for ordinal categorical data with latent factors explained by auxiliary variables applied to the Major Depression Inventory. Journal of Applied Statistics, 51(14), 2866–2893. https://doi.org/10.1080/02664763.2024.2321913
Yong, A. G., & Pearce, S. (2013). A Beginner's Guide to Factor Analysis: Focusing on Exploratory Factor Analysis. Tutorials in Quantitative Methods for Psychology, 9(2), 79–94. https://doi.org/10.20982/tqmp.09.2.p079
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Oleksandr Halushko
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
