Feature Selection for Better Identification of Subtypes of Guillain-Barré Syndrome

Joint Authors

Hernández-Torruco, José
Canul-Reich, Juana
Frausto-Solís, Juan
Méndez-Castillo, Juan José

Source

Computational and Mathematical Methods in Medicine

Issue

Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-9, 9 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2014-09-14

Country of Publication

Egypt

No. of Pages

9

Main Subjects

Medicine

Abstract EN

Guillain-Barré syndrome (GBS) is a neurological disorder which has not been explored using clustering algorithms.

Clustering algorithms perform more efficiently when they work only with relevant features.

In this work, we applied correlation-based feature selection (CFS), chi-squared, information gain, symmetrical uncertainty, and consistency filter methods to select the most relevant features from a 156-feature real dataset.

This dataset contains clinical, serological, and nerve conduction tests data obtained from GBS patients.

The most relevant feature subsets, determined with each filter method, were used to identify four subtypes of GBS present in the dataset.

We used partitions around medoids (PAM) clustering algorithm to form four clusters, corresponding to the GBS subtypes.

We applied the purity of each cluster as evaluation measure.

After experimentation, symmetrical uncertainty and information gain determined a feature subset of seven variables.

These variables conformed as a dataset were used as input to PAM and reached a purity of 0.7984.

This result leads to a first characterization of this syndrome using computational techniques.

American Psychological Association (APA)

Hernández-Torruco, José& Canul-Reich, Juana& Frausto-Solís, Juan& Méndez-Castillo, Juan José. 2014. Feature Selection for Better Identification of Subtypes of Guillain-Barré Syndrome. Computational and Mathematical Methods in Medicine،Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-1016800

Modern Language Association (MLA)

Hernández-Torruco, José…[et al.]. Feature Selection for Better Identification of Subtypes of Guillain-Barré Syndrome. Computational and Mathematical Methods in Medicine No. 2014 (2014), pp.1-9.
https://search.emarefa.net/detail/BIM-1016800

American Medical Association (AMA)

Hernández-Torruco, José& Canul-Reich, Juana& Frausto-Solís, Juan& Méndez-Castillo, Juan José. Feature Selection for Better Identification of Subtypes of Guillain-Barré Syndrome. Computational and Mathematical Methods in Medicine. 2014. Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-1016800

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1016800