Machine learning methods for classifying normal vs. tumorous tissue with spectral data
Document typeConference report
Rights accessOpen Access
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.
CitationGonzález, 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.
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