Comparison of Functional Regression and Functional Principal Component Regression for Estimating Non-Invasive Blood Glucose Level: Perbandingan Metode Regresi Fungsional dan Regresi Komponen Utama Fungsional untuk Menduga Kadar Glukosa Darah pada Alat Non-Invasif - Nurul Fadhilah; Erfiani Erfiani; Indahwati Indahwati (Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI), 2021)
The calibration method is an alternative method that can be used to analyze the relationship between invasive and non-invasive blood glucose levels. Calibration modeling generally has a large dimension and contains multicolinearities because usually in functional data the number of independent variables (p) is greater than the number of observations (p>n). Both problems can be overcome using Functional Regression (FR) and Functional Principal Component Regression (FPCR). FPCR is based on Principal Component Analysis (PCA). In FPCR, the data is transformed using a polynomial basis before data reduction. This research tried to model the equations of spectral calibration of voltage value excreted by non-invasive blood glucose level monitoring devices to predict blood glucose using FR and FPCR. This study aimed to determine the best calibration model for measuring non-invasive blood glucose levels with the FR and FPCR. The results of this research showed that the FR model had a bigger coefficient determination (R2) value and lower Root Mean Square Error (RMSE) and Root Mean Square Error Prediction (RMSEP) value than the FPCR model, which was 12.9%, 5.417, and 5.727 respectively. Overall, the calibration modeling with the FR model is the best model for estimate blood glucose level compared to the FPCR model.