Decision tree classification of proteins identified by mass spectrometry of blood serum samples from people with and without lung cancer

A classification and regression tree (CART) model was trained to classify 41 clinical specimens as disease/nondisease based on 26 variables computed from the mass‐to‐charge ratio (m/z) and peak heights of proteins identified by mass spectroscopy. The CART model built on all of the specimens (no cross‐validation) had an error rate of 4/41 = 10%. The CART model suggests that mass spectra peaks in the 8000–10 000, 20 000–30 000, 45 000–60 000, and >125 000 m/z ranges may be valuable in distinguishing between the disease/nondisease specimens. The area under the receiver operating characteristics curve was 0.80 ± 0.07 for leave‐one‐out cross‐validation.