Random subspace-based ensemble modeling for near-infrared spectral diagnosis of colorectal cancer.

The feasibility of using near-infrared (NIR) spectroscopy coupled with classifier ensemble for improving the diagnosis of colorectal cancer was explored. A total of 157 NIR spectra from the patients were recorded and partitioned into the training set and the test set. Four algorithms, i.e., Adaboost.M1, Totalboost and LPboost using decision tree as weak learners, together with random subspace method (RSM) using linear discriminant classifier (LDA) as weak learners, were used to construct diagnostic models. Some key parameters such as the size of ensemble, i.e., the number of weak learners in ensemble, and the size of each subspace in RSM, were optimized. The results indicated that, in terms of generalization ability, the RSM-based classifier outperforms all other classifiers by only 40 members with 30 features each. On the basis of 200 different training sets, model population analysis (MPA) was made. The average sensitivity and specificity of the RSM classifier were 97.4% and 95.6%, respectively. It indicates that the NIR technique combined with the RSM algorithm can serve as a potential means for automatic identification of colorectal tissues.