Leukemia Early Screening by Using NIR Spectroscopy and LAR-PLS Regression Model

Abstract

In this paper, a regression analysis method based on the combination of Least Angle Regression (LAR) and Partial Least Squares (PLS) is proposed, which uses the non-invasive characteristics of near infrared spectroscopy (NIRS) to implement early screening of leukemia patients. First, the LAR method is used to eliminate collinearity between variables, second, PLS is employed to further build model for the wavelengths which are selected by the LAR. The result shows that this method needs less wavelength points and has more excellent performance in correlation coefficient and root mean square error, that are 0.9492 and 0.5917 respectively. The comparison experiments demonstrate that the LAR-PLS regression model has an advantage over principal component regression (PCR), the LAR-PCR regression model, successive projections algorithm (SPA) and elimination of uninformative variables (UVE) combined with PLS method in terms of predictive accuracy for screening leukemia patients.