Patient sera Postop

Dukes'/TNM stage

Classification using tumour verses normal model

Classification using leaveoneout xvalidation


 
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

 The classification of each postoperative (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 knearestneighbours (kNN) algorithms were used to classify PO samples either by first generating a predictive model from a training set comprised of normal and preoperative cancer sera or else by 'leaveoneout' 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 kNN algorithm was Euclidean.