Genetic programming for classification and feature selection: analysis of 1 H nuclear magnetic resonance spectra from human brain tumour biopsies

Genetic programming (GP) is used to classify tumours based on 1 H nuclear magnetic resonance (NMR) spectra of biopsy extracts. Analysis of such data would ideally give not only a classification result but also indicate which parts of the spectra are driving the classification (i.e. feature selection). Experiments on a database of variables derived from 1 H NMR spectra from human brain tumour extracts ( n  = 75) are reported, showing GP's classification abilities and comparing them with that of a neural network. GP successfully classified the data into meningioma and non‐meningioma classes. The advantage over the neural network method was that it made use of simple combinations of a small group of metabolites, in particular glutamine, glutamate and alanine. This may help in the choice of the most informative NMR spectroscopy methods for future non‐invasive studies in patients. © 1998 John Wiley & Sons, Ltd.