A Predictive Model for Guillain-Barré Syndrome Based on Single Learning Algorithms
Joint Authors
Hernández-Torruco, José
Canul-Reich, Juana
Frausto-Solís, Juan
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-04-11
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
Background.
Guillain-Barré Syndrome (GBS) is a potentially fatal autoimmune neurological disorder.
The severity varies among the four main subtypes, named as Acute Inflammatory Demyelinating Polyneuropathy (AIDP), Acute Motor Axonal Neuropathy (AMAN), Acute Motor Sensory Axonal Neuropathy (AMSAN), and Miller-Fisher Syndrome (MF).
A proper subtype identification may help to promptly carry out adequate treatment in patients.
Method.
We perform experiments with 15 single classifiers in two scenarios: four subtypes’ classification and One versus All (OvA) classification.
We used a dataset with the 16 relevant features identified in a previous phase.
Performance evaluation is made by 10-fold cross validation (10-FCV).
Typical classification performance measures are used.
A statistical test is conducted in order to identify the top five classifiers for each case.
Results.
In four GBS subtypes’ classification, half of the classifiers investigated in this study obtained an average accuracy above 0.90.
In OvA classification, the two subtypes with the largest number of instances resulted in the best classification results.
Conclusions.
This study represents a comprehensive effort on creating a predictive model for Guillain-Barré Syndrome subtypes.
Also, the analysis performed in this work provides insight about the best single classifiers for each classification case.
American Psychological Association (APA)
Canul-Reich, Juana& Frausto-Solís, Juan& Hernández-Torruco, José. 2017. A Predictive Model for Guillain-Barré Syndrome Based on Single Learning Algorithms. Computational and Mathematical Methods in Medicine،Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1142380
Modern Language Association (MLA)
Canul-Reich, Juana…[et al.]. A Predictive Model for Guillain-Barré Syndrome Based on Single Learning Algorithms. Computational and Mathematical Methods in Medicine No. 2017 (2017), pp.1-9.
https://search.emarefa.net/detail/BIM-1142380
American Medical Association (AMA)
Canul-Reich, Juana& Frausto-Solís, Juan& Hernández-Torruco, José. A Predictive Model for Guillain-Barré Syndrome Based on Single Learning Algorithms. Computational and Mathematical Methods in Medicine. 2017. Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1142380
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1142380