Near infrared spectroscopy combined with least squares support vector machines and fuzzy rule-building expert system applied to diagnosis of endometrial carcinoma.

OBJECTIVE The feasibility of early diagnosis of endometrial carcinoma was studied by least squares support vector machines (LS-SVM) and fuzzy rule-building expert system (FuRES) that classified near infrared (NIR) spectra of tissues. METHODS NIR spectra of 77 specimens of endometrium were collected. The spectra were pretreated by principal component orthogonal signal correction (PC-OSC) and direct orthogonal signal correction (DOSC) methods to improve the signal-to-noise ratio (SNR) and remove the influences of background and baseline. The effects of modeling parameters were investigated using bootstrapped Latin-partition methods. RESULTS The optimal LS-SVM model of the PC-OSC pretreatment method successfully classified the samples with prediction accuracies of 96.8±1.4%. CONCLUSIONS The proposed procedure proved to be rapid and convenient, which is suitable to be developed as a non-invasive diagnosis method for cancer tissue.