ESTIMASI SINTASAN PENDERITA DIABETES MELITUS: KOMPARASI KINERJA REGRESI PLS DAN LASSO

Abstract

Partial least squares (PLS) regression and least absolute shrinkage and selection operator (LASSO) are the regression analysis techniques used to overcome the problems that can not be solved by ordinary least squares (OLS). The purpose of this research is to model and compare the performance of both PLS regression and LASSO to the diabetes mellitus study data which is divided into 30 groups of data redundancy as an example of microarray data. The survival time of diabetes mellitus patients as dependent variable while age, sex, body mass index, blood pressure, and six blood serum measurements as independent variables. By using paired sample t-test of adj R2 value, the result of this research concluded that the mean of adj R2 value of PLS regression is smaller than the mean of adj R2 value of LASSO. In other words, the performance of LASSO is better than PLS regression.