Terahertz time-domain spectroscopy combined with support vector machines and partial least squares-discriminant analysis applied for the diagnosis of cervical carcinoma

Coupled with terahertz time-domain spectroscopy (THz-TDS) technology, the feasibility for the diagnosis of cervical carcinoma using support vector machines (SVM) and partial least squares-discriminant analysis (PLS-DA) had been studied. The terahertz spectra of 52 specimens of cervix were collected. The performance of the preprocessing methods of multiplicative scatter correction (MSC), Savitzky-Golay (SG) smoothing and first derivative, principal component orthogonal signal correction (PC-OSC) and emphatic orthogonal signal correction (EOSC) were investigated for PLS-DA and SVM models. The effects of the different pretreatment methods with respect to classification accuracy were compared. The PLS-DA and SVM models were validated using the bootstrapped Latin-partition method. The SVM and PLS-DA models optimized with the combination of SG first derivative and PC-OSC preprocessing had the best predictive results with classification rates of 94.0% +/- 0.4% and 94.0% +/- 0.5%, respectively. The proposed procedure proved that terahertz spectroscopy combined with classifiers provides a technology that has potential as a new diagnosis method for cancer tissue.