Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Fuzzy Logic System for Classifying Multiple Sclerosis Patients: High, Medium or Low Responder to Interferon-Beta

Edgar Ponce de Leon-Sanchez
* ORCID logo ,
Jorge Mendiola-Santibañez
* ORCID logo ,
Omar Dominguez-Ramirez
ORCID logo ,
Ana Herrera-Navarro
,
Alberto Vazquez-Cervantes
,
Hugo Jimenez-Hernandez
ORCID logo ,
Horacio Senties-Madrid
Version 1 : Received: 17 July 2023 / Approved: 20 July 2023 / Online: 21 July 2023 (09:08:26 CEST)

A peer-reviewed article of this Preprint also exists.

Ponce de Leon-Sanchez, E.R.; Mendiola-Santibañez, J.D.; Dominguez-Ramirez, O.A.; Herrera-Navarro, A.M.; Vazquez-Cervantes, A.; Jimenez-Hernandez, H.; Senties-Madrid, H. Fuzzy Logic System for Classifying Multiple Sclerosis Patients as High, Medium, or Low Responders to Interferon-Beta. Technologies 2023, 11, 109. Ponce de Leon-Sanchez, E.R.; Mendiola-Santibañez, J.D.; Dominguez-Ramirez, O.A.; Herrera-Navarro, A.M.; Vazquez-Cervantes, A.; Jimenez-Hernandez, H.; Senties-Madrid, H. Fuzzy Logic System for Classifying Multiple Sclerosis Patients as High, Medium, or Low Responders to Interferon-Beta. Technologies 2023, 11, 109.

Abstract

Interferon-beta is one of the most widely prescribed disease-modifying therapies for multiple sclerosis patients. However, this treatment is only partially effective, and a significant proportion of patients do not respond to this drug. This paper proposes an alternative fuzzy logic system based on the opinion of a neurology expert to classify relapsing-remitting multiple sclerosis patients: high, medium, and low responder to interferon-beta. Also, a pipeline prediction model trained with biomarkers associated to interferon-beta response is proposed for predicting whether patients are potential candidates to be treated with this drug, in order to avoid ineffective therapies. The classification results shows that the fuzzy system presents a 100% efficiency compared with an unsupervised hierarchical clustering method (52%). So, the performance of the prediction model is evaluated, and a 0.8 testing accuracy is achieved. Hence, a pipeline model including data standardization, data compression, and a learning algorithm, can be a useful tool for getting reliable predictions about the response to interferon-beta.

Keywords

fuzzy logic system; pipeline prediction model; multiple sclerosis

Subject

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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