Application of UV-VIS Spectroscopy and Machine Learning Methods in Glucosuria Diagnostics: A Phantom Study

Maria Babińska; Adam Władziński

doi:10.4302/plp.v17i1.1319

Application of UV-VIS Spectroscopy and Machine Learning Methods in Glucosuria Diagnostics: A Phantom Study

Abstract

In this study, UV-VIS spectroscopy was used as a tool for detecting low glucose concentrations in urine. Measurements were performed on artificial urine samples and solutions with 0.1% and 0.2% glucose, covering both normal and pathological thresholds. Among the evaluated models, Random Forest reached 0.887for the 0.1% glucose sample, while Logistic Regression achieved 0.7796 for the 0.2% glucose sample, demonstrating high effectiveness in distinguishing glucose levels.The results confirm that the integration of UV-VIS spectroscopy and machine learning has the potential to serve as a fast and non-invasive screening tool for the early detection of metabolic disorders.

Citations

0

CrossRef
0

Web of Science
0

Scopus

Authors (2)

Cite as

Full text

download paper

downloaded 0 times

Publication version: Accepted or Published Version
DOI:: Digital Object Identifier (open in new tab) 10.4302/plp.v17i1.1319
License: open in new tab

Keywords

Details

Category:

Articles

Type:

artykuły w czasopismach

Published in:

Photonics Letters of Poland no. 17, pages 16 - 19,
ISSN: 2080-2242

Language:

English

Publication year:

2025

Bibliographic description:

Babińska M., Władziński A.: Application of UV-VIS Spectroscopy and Machine Learning Methods in Glucosuria Diagnostics: A Phantom Study// Photonics Letters of Poland -Vol. 17,iss. 1 (2025), s.16-19

DOI: