Diagnosis of lung cancer based on metal contents in serum and hair using multivariate statistical methods

Abstract

The classification of normal and cancer groups with four multivariate methods according to metal contents in serum and hair samples has been discussed in the present paper. Results show that the four multivariate methods, stepwise discrimination analysis, principal components analysis, hierarchical cluster analysis, and stepwise cluster analysis can distinguish the two groups correctly. The independent samples of both normal and cancer groups were tested and can be distinguished correctly by the four methods. Therefore, these methods can be used as an aid for diagnosis of lung cancer according to the metal contents in serum and hair samples.