New methodological approaches for the treatment of imprecise data: integration of fuzzy logic and R-Shiny
Article Sidebar
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
The total or partial reproduction of the contents of the journal for educational, research, or academic purposes is authorized as long as the source is cited. For reproduction for other purposes, express authorization from the Sello Editorial Universidad de Medellín is required.
Main Article Content
Abstract
This study proposes a methodology based on fuzzy logic for the analysis of biotechnological data with uncertainty, applied to the characterization and classification of filamentous fungi strains. The methodology aims to optimize the representation of variability in experimental data and enable better interpretation compared to traditional statistical approaches.
A case study is presented focusing on the mycelial growth of different fungal strains, considering parameters such as expansion rate, structural density, and morphometric dimensions. Fisher’s optimal partition was used to model this information, defining classification ranges and subsequently estimating the fuzzy sets. Once the fuzzy variables were established, the data were transformed into fuzzy numbers and represented in contingency tables for structured analysis.
To assess the relationship between the strains and their growth categories, Fisher’s exact test was employed, allowing the determination of statistical significance of the associations among the fuzzy variables obtained. This strategy improves the characterization of mycelial growth by capturing gradual transitions and reducing information loss associated with rigid segmentation.
As an innovative contribution, an R-Shiny web application was developed to facilitate interactive data analysis without requiring advanced programming skills. The results show that fuzzy modeling enhances the interpretation and classification of fungal strains and provides a replicable methodological framework in biotechnology and other fields with data uncertainty.
Article Details
References
R. Krishnapuram, Fuzzy Data Analysis — Methods and Industrial Applications. Elsevier, 2024. doi: 10.1016/0165-0114(94)90280-1.
C. Kahraman, B. Oztaysi, I. Otay, y S. C. Onar, “Extensions of ordinary fuzzy sets: a comparative literature review,” en Intelligent and Fuzzy Techniques: Smart and Innovative Solutions: Proceedings of the INFUS 2020 Conference, Springer, 2020, pp. 1655–1665. doi:
1007/978-3-030-51156-2_193.
S. M. Baas, Feasible Algorithms for Approximate Reasoning Using Fuzzy Logic. Elsevier, 2024. doi: 10.1016/0165-0114(80)90022-6.
E. E. Zurek, D. J. Romero, H. D. Blanco, y C. A. Gutiérrez, “Fuzzy inference system for the identification of over-the-counter (OTC) drugs,” Rev. Ing. Univ. Medellín, vol. 12, no. 22, pp. 109–116, 2014. doi: 10.22395/rium.v12n22a9.
M. I. Césari, “Soluciones metodológicas para el análisis de datos imprecisos: Lógica difusa y R-Shiny,” Actas Jorn. Eventos Académicos UTN, 2024. [En línea]. Disponible en: https://rtyc.utn.edu.ar/index.php/ajea/article/download/1738/1565/8194.
C. Swathi, J. J. Ebienazer, M. Swathi, y S. Suruthipriya, “Fuzzy Logic,” Int. J. Innov. Res. Inf. Secur., vol. 9, no. 3, pp. 147–152, 2023. doi: 10.26562/ijiris.2023.v0903.19.
M. D. Arango Serna, C. A. Serna Urán y A. C. Gómez Builes, “Inferencia difusa aplicada a la ingeniería concurrente para el diseño de productos de manufactura en condiciones de incertidumbre,” Rev. Ing. Univ. Medellín, vol. 11, no. 21, pp. 127–138, 2012. [En línea]. Disponible en: https://www.scielo.org.co/scielo.php?pid=S1692-33242012000200011&script=sci_arttext.
M. Arroyo y J. Antolínez, “Aplicación de lógica difusa para la valoración actitudinal en estudiantes universitarios,” Rev. Electr. Investig. Educ., vol. 17, no. 1, pp. 120–136, 2015. [En línea]. Disponible en: https://www.redalyc.org/pdf/4677/467746222001.pdf.
S. Ameen, R. Balachandran, y T. Theodoridis, “Deriving Hematological Disease Classes Using Fuzzy Logic and Expert Knowledge,” arXiv preprint, 2024. [En línea]. Disponible en: https://arxiv.org/abs/2406.13015.
N. Martínez Rojas y H. H. Andrade Sosa, “Integración de la lógica difusa a la dinámica de sistemas para la selección de terrenos de cultivos agrícolas,” Rev. Investig. Des. Innov., vol. 7, no. 1, pp. 45–56, 2016. [En línea]. Disponible en: https://dialnet.unirioja.es/descarga/
articulo/5676771.pdf.
R. Core Team, R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, 2023. [En línea]. Disponible en: https://www.R-project.org/.
D. Dubois, “On statistical inference for fuzzy data with applications to descriptive statistics,” Fuzzy Sets Syst., 2024. doi: 10.1016/0165-0114(92)90213-N.
M. Kuhn, “caret: Classification and Regression Training,” 2021. [En línea]. Disponible en: https://cran.r-project.org/web/packages/caret/index.html.
W. D. Fisher, “On grouping for maximum homogeneity,” J. Am. Stat. Assoc., vol. 53, no. 284, pp. 789–798, 1958. doi: 10.1080/01621459.1958.10501479.
R. Bivand et al., “classInt: Choose Univariate Class Intervals (Version 0.4-11) [Paquete de R],” CRAN, 2025. [En línea]. Disponible en: https://cran.r-project.org/web/packages/classInt/classInt.pdf.
L. A. Zadeh, “Calibration with imprecise signals and concentrations based on fuzzy theory,” Chemom. Intell. Lab. Syst., 2024. doi: 10.1016/0169-7439(86)80027-2.
K. Al-Ghamdi, “Fuzzy data-driven scenario-based robust data envelopment analysis for prediction and optimization,” Expert Syst. Appl., vol. 116, p. 419, 2022. doi: 10.1016/j.eswa.2021.116419.
M. A. Gil, “A note on interval estimation with fuzzy data,” Fuzzy Sets Syst., 2024. doi: 10.1016/0165-0114(88)90201-1.
D. Zhang, y S. Chen, “FuzzyR: Fuzzy logic toolkit in R (Version 2.2-3) [Manual de software],” CRAN, 2023. Disponible en: https://cran.r-project.org/web/packages/FuzzyR/FuzzyR.pdf.
C. K. Macnamara, “A fuzzy mathematical model for tumor growth pattern using generalized Hukuhara derivative,” Appl. Soft Comput., vol. 122, 2022. doi: 10.1016/j.asoc.2022.108467.
E. H. Houssein, A. K. Mahmoud, A. E. Hassanien, y M. K. Emam, “Soft computing techniques for biomedical data analysis,” Artif. Intell. Rev., vol. 56, no. 2, pp. 2599–2649, 2023. doi: 10.1007/s10462-023-10585-2.
S. Patel, “Fuzzy machine learning logic utilization on hormonal imbalance dataset,” Comput. Biol. Med., vol. 151, 2024. doi: 10.1016/j.compbiomed.2024.108429.
N. Talpur, S. K. Das, S. Kumar, y J. Singh, “Deep Neuro-Fuzzy System application trends and challenges,” Artif. Intell. Rev., vol. 56, pp. 865–913, 2023. doi: 10.1007/s10462-022-10188-3. [24] K. Al-Ghamdi, “Fuzzy data-driven scenario-based robust data envelopment analysis for prediction and optimization of an electrical discharge machine’s parameters,” Expert Syst. Appl., vol. 116, 2022. doi: 10.1016/j.eswa.2021.116419.
IEEE, “A Data-Driven Fuzzy Logic Method for Psychophysiological Assessment,” IEEE Trans. Med. Robot. Bionics, 2025. doi: 10.1109/TMRB.2024.3377453.
M. D. Arango Serna, C. A. Serna Urán y A. C. Gómez Builes, “Inferencia difusa aplicada a la ingeniería concurrente para el diseño de productos de manufactura en condiciones de incertidumbre,” Rev. Ing. Univ. Medellín, vol. 11, no. 21, pp. 127–138, 2012. [En línea]. Disponible en: https://www.scielo.org.co/scielo.php?pid=S1692-33242012000200011&script=sci_arttext.
A. Jabiyeva y M. Khudaverdiyeva, “Application of Fuzzy Logic in Computer Systems of Medical Diagnosis,” Socio World Social Research & Behavioral Sciences, vol. 12, no. 02, pp. 17–25, dic. 2023, doi: 10.36962/SWD12022023-17. Disponible en: https://papers.ssrn.
com/sol3/papers.cfm?abstract_id=4667205