Machine Learning Approach for the Outcome Prediction of Temporal Lobe Epilepsy Surgery

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 ep...

Full description

Bibliographic Details
Main Authors:	Armañanzas, Rubén, Alonso-Nanclares, Lidia, DeFelipe-Oroquieta, Jesús, Kastanauskaite, Asta, de Sola, Rafael G., DeFelipe, Javier, Bielza, Concha, Larrañaga, Pedro
Format:	Online
Language:	English
Published:	Public Library of Science 2013
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3640010/

id	pubmed-3640010
recordtype	oai_dc
spelling	pubmed-36400102013-05-03 Machine Learning Approach for the Outcome Prediction of Temporal Lobe Epilepsy Surgery Armañanzas, Rubén Alonso-Nanclares, Lidia DeFelipe-Oroquieta, Jesús Kastanauskaite, Asta de Sola, Rafael G. DeFelipe, Javier Bielza, Concha Larrañaga, Pedro Research Article 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 into 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. Public Library of Science 2013-04-30 /pmc/articles/PMC3640010/ /pubmed/23646148 http://dx.doi.org/10.1371/journal.pone.0062819 Text en © 2013 Armañanzas et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Armañanzas, Rubén Alonso-Nanclares, Lidia DeFelipe-Oroquieta, Jesús Kastanauskaite, Asta de Sola, Rafael G. DeFelipe, Javier Bielza, Concha Larrañaga, Pedro
spellingShingle	Armañanzas, Rubén Alonso-Nanclares, Lidia DeFelipe-Oroquieta, Jesús Kastanauskaite, Asta de Sola, Rafael G. DeFelipe, Javier Bielza, Concha Larrañaga, Pedro Machine Learning Approach for the Outcome Prediction of Temporal Lobe Epilepsy Surgery
author_facet	Armañanzas, Rubén Alonso-Nanclares, Lidia DeFelipe-Oroquieta, Jesús Kastanauskaite, Asta de Sola, Rafael G. DeFelipe, Javier Bielza, Concha Larrañaga, Pedro
author_sort	Armañanzas, Rubén
title	Machine Learning Approach for the Outcome Prediction of Temporal Lobe Epilepsy Surgery
title_short	Machine Learning Approach for the Outcome Prediction of Temporal Lobe Epilepsy Surgery
title_full	Machine Learning Approach for the Outcome Prediction of Temporal Lobe Epilepsy Surgery
title_fullStr	Machine Learning Approach for the Outcome Prediction of Temporal Lobe Epilepsy Surgery
title_full_unstemmed	Machine Learning Approach for the Outcome Prediction of Temporal Lobe Epilepsy Surgery
title_sort	machine learning approach for the outcome prediction of temporal lobe epilepsy surgery
description	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 into 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.
publisher	Public Library of Science
publishDate	2013
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3640010/
_version_	1611973884019474432

Machine Learning Approach for the Outcome Prediction of Temporal Lobe Epilepsy Surgery

Similar Items