Decision tree based fuzzy classifier of H1 magnetic resonance spectra from cerebrospinal fluid samples

Authors:
F. X. Aymerich;J. Alonso;M. E. Cabaòas;M. Comabella;P. Sobrevilla;A. Rovira
Affiliations:
Unitat RM Vall Hebron (IDI), Hospital Vall Hebron, P. Vall Hebron 119-129, 08035 Barcelona, Spain and Dept. Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Polit ...;Unitat RM Vall Hebron (IDI), Hospital Vall Hebron, P. Vall Hebron 119-129, 08035 Barcelona, Spain and Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas ...;Fac. Ciències, Servei Ressonància Magnètica Nuclear, Universitat Autònoma Barcelona, 08193 Cerdanyola del Vallès, Spain;Unitat Neuroimmunologia Clínica, CEM Cat, Hospital Vall Hebron, P. Vall Hebron 119-129, 08035 Barcelona, Spain;Dept. Matemàtica Aplicada II, Universitat Politècnica de Catalunya, Jordi Girona 1-3, 08034 Barcelona, Spain;Unitat RM Vall Hebron (IDI), Hospital Vall Hebron, P. Vall Hebron 119-129, 08035 Barcelona, Spain
Venue:
Fuzzy Sets and Systems
Year:
2011

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Abstract

This paper presents a method for classifying cerebrospinal fluid (CSF) samples studied by proton magnetic resonance spectroscopy (H1 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.