FastGLLVM: Big Data Ordination Towards Intensive Care Event Count Cases

Abstract

Background: In the heart data mining and machine learning, dimension reduction is needed to remove the multicollinearity. Meanwhile, it has been proven to improves the interpretation of the parameter model. In addition, dimension reduction is also can increase the time of computing in high dimensional data. Methods: In this paper, we perform high dimensional ordination towards event counts in intensive care hospital , following emergency department (ED 1), First Intensive Care Unit (ICU1), Second Intensive Care Unit (ICU2), Respiratory Care Intensive Care Unit (RICU), Surgical Intensive Care Unit (SICU), Subacute Respiratory Care Unit (RCC), Trauma and Neurosurgery Intensive Care Unit (TNCU), Neonatal Intensive Care Unit (NICU) which use the Generalized Linear Latent Variable Models (GLLVM’s). Results: During the analysis, we measure the performance and calculate the time computing of GLLVM with employing variational approximation and Laplace approximation, and compare the different distributions including Negative Binomial, Poisson, Gaussian, ZIP, and Tweedi, respectively. Conclusions: In a nutshell, GLLVM’s leads as best performance reaching the accuracy 98% comparing other methods. In line with this, we get the best model negative binomial and Variational approximation which provides the best accuracy by accruacy value of AIC, AICc, and BIC. In a nutshell, our best model is GLLVM-VA Negative Binomial with AIC 7144.07 and GLLVM-LA Negative Binomial with AIC 6955.922.