Research Article
Application of Machine Learning Models for Predicting Glucose-Level in the Pure Fluid with Algorithm for Reducing Data Dimension Based on Data Series Extraction
@INPROCEEDINGS{10.1007/978-3-031-58878-5_6, author={Tri Ngo Quang and Tung Nguyen Thanh and Huong Pham Thi Viet and Huy Bui Quang}, title={Application of Machine Learning Models for Predicting Glucose-Level in the Pure Fluid with Algorithm for Reducing Data Dimension Based on Data Series Extraction}, proceedings={Context-Aware Systems and Applications. 12th EAI International Conference, ICCASA 2023, Ho Chi Minh City, Vietnam, October 26-27, 2023, Proceedings}, proceedings_a={ICCASA}, year={2024}, month={8}, keywords={Raman spectroscopy machine learning Diabetes}, doi={10.1007/978-3-031-58878-5_6} }- Tri Ngo Quang
Tung Nguyen Thanh
Huong Pham Thi Viet
Huy Bui Quang
Year: 2024
Application of Machine Learning Models for Predicting Glucose-Level in the Pure Fluid with Algorithm for Reducing Data Dimension Based on Data Series Extraction
ICCASA
Springer
DOI: 10.1007/978-3-031-58878-5_6
Abstract
The phenomenon that glucose level of pure liquid is able to define patterns of Raman spectroscopy was demonstrated in several studies. Nevertheless, it is difficult to predict glucose level accurately by manual methods so machine learning techniques are proposed to support it. In the range of the report, we employ three simple machine learning models including Extra Trees, Random Forest, and SVM to predict glucose level from Raman spectroscopy of pure water-mixed fluid which we collected by infrastructures of Vietnam National University. In addition, the Raman data was simplified by dimension reduction algorithms based on handling data series. The results show the effectiveness of the machine learning models for predicting glucose levels as well as the reduction dimension algorithms for enhancing the performance of machine learning techniques.
