A based-rule fuzzy expert system to estimate the response to treatments in multiple sclerosis patients

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Published Sep 30, 2024
DOI: https://doi.org/10.18103/mra.v12i9.5863

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Main Article Content

Edgar Rafael Ponce de León Sánchez
Facultad de Informática, Universidad Autónoma de Querétaro, México
Jorge Domingo Mendiola Santibañez
Facultad de Ingeniería, Universidad Autónoma de Querétaro, México
Omar Arturo Domínguez Ramírez
Centro de Investigación en Tecnologías de Información y Sistemas, Universidad Autónoma del Estado de Hidalgo, México
Martín Gallegos Duarte
Facultad de Medicina, Universidad Autónoma de Querétaro, México
Ana Marcela Herrera Navarro
Facultad de Informática, Universidad Autónoma de Querétaro, México
Horacio Senties Madrid
Hospital HMG Coyoacán, Ciudad de México, México
José Jonathan Franco Téllez
Instituto de Ciencias de la Salud, Universidad Autónoma del Estado de Hidalgo, México

Abstract

Background: Multiple sclerosis (MS) is an autoimmune inflammatory disease of the central nervous system (CNS) that affects about 2.9 million people worldwide. Current disease-modifying therapies are focused on delaying the disease progression, treating sensitive attacks, and improving symptoms. However, some patients partially respond or do not respond to MS treatments. So, it is important to determine the degree of response of patients to treatments.


Methods: Expert systems are computer programs that attempt to emulate the reasoning process of some skills of a human expert. A fuzzy expert system incorporates fuzzy logic into its reasoning process to manage uncertain and imprecise information that a binary system could not. So, a based-rule fuzzy expert system based on the Takagi–Sugeno–Kang Fuzzy System (TSKFS) model is proposed to estimate the degree of response to different types of drugs such as Gelenia, Tysabri, Avonex, Betaferon, and Rebif in 60 MS patients, using clinical patient information derived from neurological examinations as input variables.


Results: The results of the proposed fuzzy expert system to estimate the response to MS treatments in MS patients show a high efficiency (100%) compared with conventional classification methods such as the K-Means clustering model (62%).


Conclusion: Expert systems are efficient tools for classifying the response to MS treatments and can support the decision of specialists to prescribe the most appropriate therapy for the individual patient.

Keywords: Multiple sclerosis (MS), Fuzzy expert system, Takagi–Sugeno–Kang Fuzzy System (TSKFS), Treatment response estimation, Disease-modifying therapies, Neurological examinations, MS drug response, Clinical decision support, K-Means clustering model, Autoimmune disease

Article Details

How to Cite
SÁNCHEZ, Edgar Rafael Ponce de León et al. A based-rule fuzzy expert system to estimate the response to treatments in multiple sclerosis patients. Medical Research Archives, [S.l.], v. 12, n. 9, sep. 2024. ISSN 2375-1924. Available at: <https://esmed.org/MRA/mra/article/view/5863>. Date accessed: 21 nov. 2024. doi: https://doi.org/10.18103/mra.v12i9.5863.
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Vol 12 No 9 (2024): Vol 12 No 9 (2024): September ISSUE, Issue 9, VOl.12
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