| Títol: | Machine learning methods for classifying normal vs. tumorous tissue with spectral data |
| Autor: | González Navarro, Félix Fernando
Belanche Muñoz, Luis Antonio |
| Altres autors/autores: | Universitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics |
| Matèries: | 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 |
| Tipus de document: | Conference report |
| Descripció: | 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. |
| Altres identificadors i accés: | González, F.F.; Belanche, Ll. Machine learning methods for classifying normal vs. tumorous tissue with spectral data. A: Congreso Internacional de Informática y Computación. "VIII Congreso Internacional de Informática y Computación (ANIEI 2009)". Ensenada: 2009.
978-607-7854-36-4
http://hdl.handle.net/2117/16220 |
| Disponible al dipòsit: | E-prints UPC
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