Integrating Laser-Induced Breakdown Spectroscopy and Ensemble Learning as Minimally Invasive Optical Screening for Diabetes

Diabetes mellitus is a prevalent chronic disease necessitating timely identification for effective management. This paper introduces a reliable, straightforward, and efficient method for the minimally invasive identification of diabetes mellitus through nanosecond pulsed laser-induced breakdown spec...

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Bibliographic Details
Published inApplied spectroscopy Vol. 78; no. 11; p. 1154
Main Authors Rehan, Imran, Khan, Saranjam, Ullah, Rahat
Format Journal Article
LanguageEnglish
Published United States 01.11.2024
Subjects
ensemble learning
Uncontrolled diabetes
LIBS
urinary electrolytes
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Summary:Diabetes mellitus is a prevalent chronic disease necessitating timely identification for effective management. This paper introduces a reliable, straightforward, and efficient method for the minimally invasive identification of diabetes mellitus through nanosecond pulsed laser-induced breakdown spectroscopy (LIBS) by integrating a state-of-the-art machine learning approach. LIBS spectra were collected from urine samples of diabetic and healthy individuals. Principal component analysis and an ensemble learning classification model were used to identify significant changes in LIBS peak intensity between the diseased and normal urine samples. The model, integrating six distinct classifiers and cross-validation techniques, exhibited high accuracy (96.5%) in predicting diabetes mellitus. Our findings emphasize the potential of LIBS for diabetes mellitus identification in urine samples. This technique may hold potential for future applications in diagnosing other health conditions.
ISSN:1943-3530
DOI:10.1177/00037028241278902