Decision tree based fuzzy classifier of magnetic resonance spectra from cerebrospinal fluid samples

This paper presents a method for classifying cerebrospinal fluid (CSF) samples studied by proton magnetic resonance spectroscopy ( MRS) into clinical subgroups by means of a fuzzy classifier. The method focuses on the analysis of a low signal-to-noise region of the spectra and is designed to use a small number of samples because sampling can only be done through an invasive technique. The proposed method involves the fusion of classifiers based on decision trees designed using fuzzy techniques. The fusion step was carried out by ordered weighted averaging (OWA) operators. The quality of the proposed classifier was evaluated by efficiency and robustness quality indexes using a method based on a cross-validation technique. Results show excellent classification levels and satisfactory robustness in both training and test sets.