To access the full text documents, please follow this link: http://hdl.handle.net/2117/16220

Machine learning methods for classifying normal vs. tumorous tissue with spectral data
González Navarro, Félix Fernando; Belanche Muñoz, Luis Antonio
Universitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics; Universitat Politècnica de Catalunya. SOCO - Soft Computing
Machine learning is a powerful paradigm within which to analyze 1H-MRS spectral data for the automated classi¯cation of tumor pathologies aimed to facilitate clinical diagnosis. The high dimensionality of the involved data sets makes the discover of computational models a challenging task. In this study we apply a feature selection algorithm in order to reduce the complexity of the problem. The obtained experimental results yield a remarkable classification performance of the final induced models, both in terms of prediction accuracy and number of involved spectral frequencies. A dimensionality reduction technique that preserves the class discrimination capabilities is used for the visualization of the final selected frequencies, thus enhancing their interpretability.
Peer Reviewed
Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Bioinformàtica
Machine learning
Proton magnetic resonance spectroscopy
Tumors -- Classification
Brain tumor classification
Feature Selection
Visualization
Aprenentatge automàtic
Tumors -- Classificació
Cervell -- Tumors
Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
info:eu-repo/semantics/submittedVersion
info:eu-repo/semantics/conferenceObject
         

Show full item record

Related documents

Other documents of the same author

González Navarro, Félix Fernando; Belanche Muñoz, Luis Antonio
González Navarro, Félix Fernando; Belanche Muñoz, Luis Antonio
González Navarro, Félix Fernando; Belanche Muñoz, Luis Antonio
González Navarro, Félix Fernando; Belanche Muñoz, Luis Antonio; Silva Colón, Karen Andrea
 

Coordination

 

Supporters