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Table 2 Classification of post-operative sera by supervised learning

From: Analysis of post-operative changes in serum protein expression profiles from colorectal cancer patients by MALDI-TOF mass spectrometry: a pilot methodological study

Patient sera Post-op Dukes'/TNM stage Classification using tumour verses normal model Classification using leave-one-out x-validation
   k -NN Conf. WV Conf. k -NN Conf. WV Conf.
PO1 B (pT3) N 0.3828 N 0.4044 N 0.0723 N 0.3997
PO2 A (pT2) N 0.6741 N 0.3174 N 0.2472 N 0.0249
PO3 B (pT3) N 0.0891 N 0.0448 N 0.2052 N 0.1
PO4 A (pT2) N 0.3015 N 0.8506 N 0.4608 N 0.2614
PO5 C1 (pT3, pN1) T 0.11 T 0.2675 T 0.1291 T 0.1098
PO6 C1 (pT4, N1) T 0.418 T 0.3068 T 0.5647 T 0.4638
PO7 C2 (pT2, pN2) T 0.114 T 1.0 T 0.1973 T 0.1213
PO8 C2 (pT4, N1) T 0.7767 T 0.572 T 0.0559 T 0.1747
PO9 B (pT4) T 0.6758 T 0.7736 T 0.5172 T 0.0306
PO10 C1 (pT3, pN2) T 0.234 T 0.2663 T 0.4345 T 0.5141
PO11 B (pT4) T 0.8295 T 0.1734 T 0.5647 T 0.0475
  1. The classification of each post-operative (PO) serum sample as being either normal (N) or cancer (T) is shown together with the confidence value (conf.) representing the proportion of 'votes' assigned to the predicted class [25]. The weighted voting (WV) and k-nearest-neighbours (k-NN) algorithms were used to classify PO samples either by first generating a predictive model from a training set comprised of normal and pre-operative cancer sera or else by 'leave-one-out' cross validation using the complete set of spectra. The feature selection statistics used for both algorithms was SNR; distance measure between each feature for the k-NN algorithm was Euclidean.