Derivative-Based Mir Spectroscopy for Blood Glucose Estimation Using Pca-Driven Regression Models

TL;DR

This paper introduces two derivative-based methods, TBD and ADPD, that improve blood glucose estimation accuracy from MIR spectroscopy data using Ridge Regression and SVR models, outperforming conventional techniques.

Contribution

The study presents novel derivative-based preprocessing methods, TBD and ADPD, that enhance the performance of regression models for blood glucose estimation from MIR spectroscopy data.

Findings

TBD increased SVR R2 score by 27%.

ADPD increased SVR R2 score by 10%.

Methods achieved lower error rates and better clinical accuracy.

Abstract

In this study, we presented two innovative methods, which are Threshold-Based Derivative (TBD) and Adaptive Derivative Peak Detection(ADPD), that enhance the accuracy of Learning models for blood glucose estimation using Mid-Infrared (MIR) spectroscopy. In these presented methods, we have enhanced the model's accuracy by integrating absorbance data and its differentiation with critical points. Blood samples were characterized with Fourier Transform Infrared (FTIR) spectroscopy and advanced preprocessing steps. The learning models were Ridge Regression and Support Vector Regression(SVR) using Leave-One-out Cross-Validation. Results exhibited that TBD and ADPD significantly outperform basic used methods. For SVR, the TBD increased the r2 score by around 27%, and ADPD increased it by around 10%. these Ridge Regression values were between 36% and 24%. In addition, Results demonstrate that TBD and ADPD significantly outperform conventional methods, achieving lower error rates and improved clinical accuracy, validated through Clarke and Parkes Error Grid Analysis.