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

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...

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Bibliographic Details
Published in:Technologies (Basel) 2023-08, Vol.11 (4), p.109
Main Authors: Ponce de Leon-Sanchez, Edgar Rafael, Mendiola-Santibanez, Jorge Domingo, Dominguez-Ramirez, Omar Arturo, Herrera-Navarro, Ana Marcela, Vazquez-Cervantes, Alberto, Jimenez-Hernandez, Hugo, Senties-Madrid, Horacio
Format: Article
Language:English
Subjects:
Algorithms
Antigens
Biomarkers
Care and treatment
Classification
Cluster analysis
Clustering
Comparative analysis
Cytokines
Data compression
Disease
Drug dosages
Fuzzy algorithms
Fuzzy logic
fuzzy logic system
Fuzzy sets
Fuzzy systems
Gene expression
Genomes
Genotype & phenotype
Health aspects
Identification and classification
Immune system
Interferon
Interferon beta
Lymphocytes
Machine learning
Multiple sclerosis
Neurology
Patients
Physiological aspects
pipeline prediction model
Prediction models
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Summary: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 as high, medium, or low responders to interferon-beta. Also, a pipeline prediction model trained with biomarkers associated with interferon-beta responses is proposed, for predicting whether patients are potential candidates to be treated with this drug, in order to avoid ineffective therapies. The classification results showed that the fuzzy system presented 100% efficiency, compared to an unsupervised hierarchical clustering method (52%). So, the performance of the prediction model was evaluated, and 0.8 testing accuracy was achieved. Hence, a pipeline model, including data standardization, data compression, and a learning algorithm, could be a useful tool for getting reliable predictions about responses to interferon-beta.
ISSN:2227-7080
2227-7080
DOI:10.3390/technologies11040109