Classification of glucose-level in deionized water using machine learning models and data pre-processing technique

被引:0
|
作者
Tri Ngo Quang [1 ,2 ]
Tung Nguyen Thanh [1 ,3 ]
Duc Le Anh [1 ]
Huong Pham Thi Viet [1 ]
Doanh Sai Cong [4 ]
机构
[1] Vietnam Natl Univ, Int Sch, Hanoi, Vietnam
[2] Univ Econ Technol Ind, Fac Informat Technol, Hanoi, Vietnam
[3] Nguyen Tat Thanh Univ, Fac IT, Ho Chi Minh City, Vietnam
[4] Vietnam Natl Univ, Univ Sci, Hanoi, Vietnam
来源
PLOS ONE | 2024年 / 19卷 / 12期
关键词
ALGORITHM;
D O I
10.1371/journal.pone.0311482
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Accurate monitoring of glucose levels is essential in the field of diabetes detection and prevention to ensure appropriate treatment planning. Conventional blood glucose monitoring methods, although widely used, are intrusive and frequently result in discomfort. This study investigates the use of Raman spectroscopy as a non-invasive method for estimating glucose concentrations. Our proposition entails employing machine learning models to categorize glucose levels by utilizing Raman spectrum data. The collection consists of deionized water samples containing glucose with defined amounts, guaranteeing great purity and little interference. We assess the efficacy of three machine learning models in categorizing glucose levels which including Extra Trees, Random Forest, and Support Vector Machine (SVM). In addition, we employ data pre-processing techniques such as fluorescence background removal and hotspot series extraction to improve the performance of the model. The primary results demonstrate that the utilization of these pre-processing techniques greatly enhances the accuracy of classification. Among these techniques, the Extra Trees model achieves the highest accuracy, reaching 95%. This study showcases the viability of employing machine learning techniques to forecast glucose levels based on Raman spectroscopy data. Additionally, it emphasizes the significance of data pre-processing in enhancing the accuracy of the model's results.
引用
收藏
页数:19
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