Machine learning approach for the outcome prediction of temporal lobe epilepsy surgery

Armañanzas Arnedillo, Ruben, Alonso Nanclares, Lidia, DeFelipe-Oroquieta, Jesús, Kastanauskaite, Asta, García de Sola, Rafael, DeFelipe, Javier, Bielza Lozoya, María Concepción ORCID: https://orcid.org/0000-0001-7109-2668 and Larrañaga Múgica, Pedro María ORCID: https://orcid.org/0000-0002-1885-4501 (2013). Machine learning approach for the outcome prediction of temporal lobe epilepsy surgery. "Plos One", v. 8 (n. 4); pp. 1-9. ISSN 1932-6203. https://doi.org/10.1371/journal.pone.0062819.

Description

Title: Machine learning approach for the outcome prediction of temporal lobe epilepsy surgery
Author/s:
  • Armañanzas Arnedillo, Ruben
  • Alonso Nanclares, Lidia
  • DeFelipe-Oroquieta, Jesús
  • Kastanauskaite, Asta
  • García de Sola, Rafael
  • DeFelipe, Javier
  • Bielza Lozoya, María Concepción https://orcid.org/0000-0001-7109-2668
  • Larrañaga Múgica, Pedro María https://orcid.org/0000-0002-1885-4501
Item Type: Article
Título de Revista/Publicación: Plos One
Date: April 2013
ISSN: 1932-6203
Volume: 8
Subjects:
Faculty: Facultad de Informática (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Epilepsy surgery is effective in reducing both the number and frequency of seizures, particularly in temporal lobe epilepsy (TLE). Nevertheless, a significant proportion of these patients continue suffering seizures after surgery. Here we used a machine learning approach to predict the outcome of epilepsy surgery based on supervised classification data mining taking in to account not only the common clinical variables, but also pathological and neuropsychological evaluations. We have generated models capable of predicting whether a patient with TLE secondary to hippocampal sclerosis will fully recover from epilepsy or not. The machine learning analysis revealed that outcome could be predicted with an estimated accuracy of almost 90% using some clinical and neuropsychological features. Importantly, not all the features were needed to perform the prediction; some of them proved to be irrelevant to the prognosis. Personality style was found to be one of the key features to predict the outcome. Although we examined relatively few cases, findings were verified across all data, showing that the machine learning approach described in the present study may be a powerful method. Since neuropsychological assessment of epileptic patients is a standard protocol in the pre-surgical evaluation, we propose to include these specific psychological tests and machine learning tools to improve the selection of candidates for epilepsy surgery.

Funding Projects

Type
Code
Acronym
Leader
Title
Government of Spain
SAF2009-09394
Unspecified
Unspecified
Unspecified
Government of Spain
TIN2010-20900-C04-04
Unspecified
Unspecified
Unspecified

More information

Item ID: 72858
DC Identifier: https://oa.upm.es/72858/
OAI Identifier: oai:oa.upm.es:72858
DOI: 10.1371/journal.pone.0062819
Official URL: https://journals.plos.org/plosone/article?id=10.13...
Deposited by: Biblioteca Facultad de Informatica
Deposited on: 06 Mar 2023 14:14
Last Modified: 07 Mar 2023 07:12
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