PERFORMANCE OF LASSO AND ELASTIC-NET METHODS ON NON-INVASIVE BLOOD GLUCOSE MEASUREMENT CALIBRATION MODELING

  • Farah Abqorunnisa Department of Statistics, Faculty of Mathematics and Natural Sciences, IPB University, Indonesia
  • Erfiani Erfiani Department of Statistics, Faculty of Mathematics and Natural Sciences, IPB University, Indonesia
  • Anik Djuraidah Department of Statistics, Faculty of Mathematics and Natural Sciences, IPB University, Indonesia
DOI: https://doi.org/10.30598/barekengvol17iss1pp0037-0042
Keywords: Diabetes Mellitus, Non-Invasive, Calibration, Lasso, Elastic Net

Abstract

Diabetes Mellitus (DM) is a disease that can occur in humans caused by conditions of high blood glucose levels (hyperglycemia). Detection of blood glucose levels can be done using invasive methods (injuring) and non- invasive methods (with infrared rays). Analytical methods are needed to model these results to obtain estimates of blood glucose levels. An alternative approach that can be used to analyze the relationship between invasive and non- invasive blood glucose levels is the calibration model. Problems that often occur in calibration modelling are multicollinearity and outliers. These problems can be overcome by adding new data, applying principal component analysis, and using LASSO and Elastic-Net regression to overcome calibration problems. The research data used was invasive and non-invasive blood glucose data in 2019, with as many as 74 respondents. The results of the study concluded that the summarization of the trapezoidal area in calibration modelling provides a good estimate. The performance of the Elastic Net method provides better prediction results than other models, with an RMSE value of

22.39. It has the most significant positive correlation value of 0.97, which means close to 1 so that the performance of the Elastic Net method can handle calibration modelling.

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References

J. Jiang, Linier and Generalized Liniear Mixed Models and Their Applications, Springer, New York, 2007.

S. A. P, P. Anugrah, A. Dharma, C. Wijaya and W. Lorraine McCarty, Patofisiologi : konsep klinis proses-proses penyakit, Jakarta, 1995.

T. Naes, T. Isaksson, T. Fearn and T. Davies, A User-Friendly Guide to Multivariate Calibration and Classification, NIR Publications, Chichester, 2002.

Avan, "Pendugaan Konsentrasi Glukosa dalam Darah Menggunakan Jaringan Syaraf Tiruan pada Alat Non-Invasive," Repository IPB, 2017.

R. H. Nur, "Pemodelan Kalibrasi Alat Non-Invasive Glukosa dalam Darah dengan Pendekatan Regresi Spline," Repository IPB, 2019.

Rosni, "Pendekatan regresi komponen utama dan regresi kuadrat terkecil parsial untuk menduga kadar glukosa darah non invasif," Repository IPB, 2019.

L. Rassiyanti, "Analisis Regresi Ridge Robust pada Pengukuran Kadar Glukosa Darah Non-Invasif," Repository IPB, 2020.

Herianti, "Pemodelan Kalibras Regresi Kekar (Pengukuran Kadar Glukosa Darah Non-Invasif)," Repository IPB, 2020.

Y. Sinaga, "Penerapan Metode Loopy Multiplicative Scatter Correction Dan Orthogonal Signal Correction Pada Spektrum Keluaran Alat Non-Invasif," Repository IPB, 2019.

T. T. R. &. F. J. Hastie, The Elements of Statistical Learning: Data Mining, Inference and Prediction (2nd ed.), Springer, New York, 2009.

R. Tibshirani, "Regression Shrinkage and Selection via the Lasso," Journal of the Royal Statistical Society. Series B (Methodological), pp. 267-288, 1996.

H. Zou and T. Hastie, "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B (Statistical Methodology), pp. 301-320, 2005.

M. Y. a. Y. Lin, "Model Selection and Estimation in Regression with Grouped Variables," Journal of the Royal Statistical Society. Series B (Statistical Methodology), pp. 49-67, 2006.

F. M. Sari, K. A. Notofiputro and B. Sartono, "Analisis Tingkat Kemiskinan Di Provinsi Sumatera Barat Melalui Pendekatan Regresi Terkendala (Ridge Regression, LASSO, dan Elastic Net)," STATISTIKA: Forum Teori dan Aplikasi Statistika, pp. 29-36, 2021.

Sudjana, Metoda Statistika, Bandung: Tarsito, 2005.

Published
2023-04-15
How to Cite
[1]
F. Abqorunnisa, E. Erfiani, and A. Djuraidah, “PERFORMANCE OF LASSO AND ELASTIC-NET METHODS ON NON-INVASIVE BLOOD GLUCOSE MEASUREMENT CALIBRATION MODELING”, BAREKENG: J. Math. & App., vol. 17, no. 1, pp. 0037-0042, Apr. 2023.
Issue
Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Section
Articles

Copyright (c) 2023 Farah Abqorunnisa, Erfiani Erfiani, Anik Djuraidah

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