Comparison of the performance of linear multivariate analysis methods for normal and dyplasia tissues differentiation using autofluorescence spectroscopy.
We compared the performance of three widely used linear multivariate methods for autofluorescence spectroscopic tissues differentiation. Principal component analysis (PCA), partial least squares (PLS), and multivariate linear regression (MVLR) were compared for differentiating at normal, tubular adenoma/epithelial dysplasia and cancer in colorectal and oral tissues. The methods' performances were evaluated by cross-validation analysis. The group-averaged predictive diagnostic accuracies were 85% (PCA), 90% (PLS), and 89% (MVLR) for colorectal tissues; 89% (PCA), 90% (PLS), and 90% (MVLR) for oral tissues. This study found that both PLS and MVLR achieved higher diagnostic results than did PCA.
Chu
Shou Chia
SC
Institute of Biomedical Engineering, National Yang-Ming University, Taipei, Taiwan ROC. d49004003@ym.edu.tw
Hsiao
Tzu-Chien Ryan
TC
Lin
Jen K
JK
Wang
Chih-Yu
CY
Chiang
Huihua Kenny
HK
D000465:Algorithms; D001185:Artificial Intelligence; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D016014:Linear Models; D015999:Multivariate Analysis; D009369:Neoplasms / Q000145:classification / Q000175:diagnosis*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013050:Spectrometry, Fluorescence / Q000379:methods*