PreprintArticleVersion 1Preserved in Portico This version is not peer-reviewed
Near-Infrared Spectroscopy-Based Machine Learning Models as a Simple and Fast Tool for the Prediction of Gestational Diabetes Mellitus at Different Stages of Pregnancy
Version 1
: Received: 27 March 2024 / Approved: 28 March 2024 / Online: 29 March 2024 (05:22:21 CET)
How to cite:
Mennickent, D.; Romero-Albornoz, L.; Gutiérrez-Vega, S.; Aguayo, C.; Marini, F.; Guzmán-Gutiérrez, E.; Araya, J. Near-Infrared Spectroscopy-Based Machine Learning Models as a Simple and Fast Tool for the Prediction of Gestational Diabetes Mellitus at Different Stages of Pregnancy. Preprints2024, 2024031737. https://doi.org/10.20944/preprints202403.1737.v1
Mennickent, D.; Romero-Albornoz, L.; Gutiérrez-Vega, S.; Aguayo, C.; Marini, F.; Guzmán-Gutiérrez, E.; Araya, J. Near-Infrared Spectroscopy-Based Machine Learning Models as a Simple and Fast Tool for the Prediction of Gestational Diabetes Mellitus at Different Stages of Pregnancy. Preprints 2024, 2024031737. https://doi.org/10.20944/preprints202403.1737.v1
Mennickent, D.; Romero-Albornoz, L.; Gutiérrez-Vega, S.; Aguayo, C.; Marini, F.; Guzmán-Gutiérrez, E.; Araya, J. Near-Infrared Spectroscopy-Based Machine Learning Models as a Simple and Fast Tool for the Prediction of Gestational Diabetes Mellitus at Different Stages of Pregnancy. Preprints2024, 2024031737. https://doi.org/10.20944/preprints202403.1737.v1
APA Style
Mennickent, D., Romero-Albornoz, L., Gutiérrez-Vega, S., Aguayo, C., Marini, F., Guzmán-Gutiérrez, E., & Araya, J. (2024). Near-Infrared Spectroscopy-Based Machine Learning Models as a Simple and Fast Tool for the Prediction of Gestational Diabetes Mellitus at Different Stages of Pregnancy. Preprints. https://doi.org/10.20944/preprints202403.1737.v1
Chicago/Turabian Style
Mennickent, D., Enrique Guzmán-Gutiérrez and Juan Araya. 2024 "Near-Infrared Spectroscopy-Based Machine Learning Models as a Simple and Fast Tool for the Prediction of Gestational Diabetes Mellitus at Different Stages of Pregnancy" Preprints. https://doi.org/10.20944/preprints202403.1737.v1
Abstract
Gestational diabetes mellitus (GDM) is a hyperglycemia state that is typically diagnosed by an oral glucose tolerance test (OGTT), which is unpleasant, time-consuming, poorly reproducible, and tardy. The machine learning (ML) predictive models that have been proposed to improve GDM diagnosis are usually based on instrumental methods that take hours to get a result. Near-infrared (NIR) spectroscopy, a simple, fast and low-cost analytical technique has never been assessed for the prediction of GDM. This study aims to develop ML predictive models for GDM based on NIR spectroscopy, and to evaluate their potential as early detection or alternative screening tools according to their predictive power and time of analysis.
Serum samples from the first (before GDM diagnosis) and the second (at the time of GDM diagnosis) trimester of pregnancy were analyzed by NIR spectroscopy. Four spectral ranges were considered, and 80 pretreatments were tested for each. NIR data-based models were built with single- and multi-block ML techniques. Every model was subjected to double cross-validation. The best first and second trimester models got areas under the receiver operating characteristic curve of 0.5768 ± 0.0635 and 0.8836 ± 0.0259, respectively.
This is the first study reporting NIR spectroscopy-based methods for the prediction of GDM. The developed methods allow to predict GDM from 10 µL of serum in only 32 minutes. They are simple and fast, and have a great potential to be applied in clinical practice, especially as alternative screening tools to the OGTT at the time of GDM diagnosis.
Keywords
Obstetrics and gynecology; Vibrational spectroscopy; Bioanalytical chemistry; Artificial intelligence
Subject
Medicine and Pharmacology, Obstetrics and Gynaecology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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