Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Causal Graphical Model of Bacterial Vaginosis in Pregnant Women.

Diseases (Basel, Switzerland)·2025

Same author

Supervised Deep Learning Techniques for Image Description: A Systematic Review.

Entropy (Basel, Switzerland)·2023

Same author

Time Series Complexities and Their Relationship to Forecasting Performance.

Entropy (Basel, Switzerland)·2020

Same author

A Predictive Model for Guillain-Barré Syndrome Based on Ensemble Methods.

Computational intelligence and neuroscience·2018

Same author

Multiphase Simulated Annealing Based on Boltzmann and Bose-Einstein Distribution Applied to Protein Folding Problem.

Advances in bioinformatics·2016

Same author

Towards a predictive model for Guillain-Barré syndrome.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2016

Search research articles

Related Experiment Video

Updated: Mar 2, 2026

A Simple Approach to Induce Experimental Autoimmune Neuritis in C57BL/6 Mice for Functional and Neuropathological Assessments

A Simple Approach to Induce Experimental Autoimmune Neuritis in C57BL/6 Mice for Functional and Neuropathological Assessments

Published on: November 9, 2017

A Predictive Model for Guillain-Barré Syndrome Based on Single Learning Algorithms.

Juana Canul-Reich¹, Juan Frausto-Solís², José Hernández-Torruco¹

¹División Académica de Informática y Sistemas, Universidad Juárez Autónoma de Tabasco, Km. 1 Carretera Cunduacán, Jalpa de Méndez, Col. Esmeralda, CP 86690, Cunduacán, TAB, Mexico.

Computational and Mathematical Methods in Medicine

|May 11, 2017

Summary

This study developed predictive models for Guillain-Barré Syndrome (GBS) subtypes, achieving high accuracy. The research identified optimal classifiers for accurate GBS diagnosis and treatment.

More Related Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Automated Gait Analysis to Assess Functional Recovery in Rodents with Peripheral Nerve or Spinal Cord Contusion Injury

Automated Gait Analysis to Assess Functional Recovery in Rodents with Peripheral Nerve or Spinal Cord Contusion Injury

Published on: October 6, 2020

Related Experiment Videos

Last Updated: Mar 2, 2026

A Simple Approach to Induce Experimental Autoimmune Neuritis in C57BL/6 Mice for Functional and Neuropathological Assessments

A Simple Approach to Induce Experimental Autoimmune Neuritis in C57BL/6 Mice for Functional and Neuropathological Assessments

Published on: November 9, 2017

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Automated Gait Analysis to Assess Functional Recovery in Rodents with Peripheral Nerve or Spinal Cord Contusion Injury

Automated Gait Analysis to Assess Functional Recovery in Rodents with Peripheral Nerve or Spinal Cord Contusion Injury

Published on: October 6, 2020

Area of Science:

Neurology
Machine Learning
Medical Diagnostics

Background:

Guillain-Barré Syndrome (GBS) is a severe autoimmune neurological disorder with four main subtypes.
Accurate GBS subtype identification is crucial for timely and effective patient treatment.

Purpose of the Study:

To develop and evaluate predictive models for classifying Guillain-Barré Syndrome subtypes.
To identify the most effective single classifiers for GBS subtype prediction.

Main Methods:

Experimentation with 15 single classifiers using a dataset of 16 relevant features.
Classification performed in two scenarios: four-subtype classification and One Versus All (OvA).
Performance evaluated using 10-fold cross-validation and statistical analysis.

Main Results:

Over half of the classifiers achieved an average accuracy exceeding 0.90 in the four-subtype classification.
In OvA classification, subtypes with larger datasets yielded superior classification outcomes.
Statistical tests identified top-performing classifiers for each classification scenario.

Conclusions:

This study presents a comprehensive approach to building predictive models for GBS subtypes.
The findings offer valuable insights into selecting the best classifiers for GBS diagnosis.