Exploratory integration of near-infrared spectroscopy with clinical data: a machine learning approach for HCV detection in serum samples
BackgroundManaging chronic viral infections like Hepatitis C virus (HCV) often requires expensive healthcare resources and highly qualified personnel, making efficient diagnostic methods essential. Despite remarkable therapeutic advancements for the treatment of HCV, several challenges remain, such...
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Frontiers Media S.A.
2025-06-01
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2025.1596476/full |
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| author | Eloy Pérez-Gómez José Gómez José Gómez Jennifer Gonzalo Sergio Salgüero Daniel Riado María Luisa Casas María Luisa Gutiérrez Elena Jaime Enrique Pérez-Martínez Rafael García-Carretero Javier Ramos Conrado Fernández-Rodríguez Conrado Fernández-Rodríguez Myriam Catalá Myriam Catalá Luca Martino Óscar Barquero-Pérez |
| author_facet | Eloy Pérez-Gómez José Gómez José Gómez Jennifer Gonzalo Sergio Salgüero Daniel Riado María Luisa Casas María Luisa Gutiérrez Elena Jaime Enrique Pérez-Martínez Rafael García-Carretero Javier Ramos Conrado Fernández-Rodríguez Conrado Fernández-Rodríguez Myriam Catalá Myriam Catalá Luca Martino Óscar Barquero-Pérez |
| author_sort | Eloy Pérez-Gómez |
| collection | DOAJ |
| description | BackgroundManaging chronic viral infections like Hepatitis C virus (HCV) often requires expensive healthcare resources and highly qualified personnel, making efficient diagnostic methods essential. Despite remarkable therapeutic advancements for the treatment of HCV, several challenges remain, such as improved fast diagnostic procedures allowing universal screening.ObjectiveWe propose a novel approach that combines Near-Infrared Spectroscopy (NIRS) and clinical data with machine learning (ML) to improve Hepatitis C Virus (HCV) detection in serum samples.MethodsNIRS offers a fast, non-destructive, and residue-free alternative to traditional diagnostic methods, while ML models enable feature selection and predictive analysis. We applied L1-regularized Logistic Regression (L1-LR) to identify the most informative wavelengths for HCV detection within the 1,000–2,500 nm range, and then integrated these spectral features with routine clinical markers using a Random Forest (RF) model. Our dataset comprised 137 serum samples from 38 patients, each represented by a NIRS spectrum and clinical data from blood tests.ResultsAfter preprocessing with Standard Normal Variate (SNV) correction and downsampling, the best-performing RF model, which combined NIRS features and clinical data, achieved an accuracy of 72.2% and an AUC-ROC of 0.850, outperforming models using only clinical or spectral data. Feature importance analysis highlighted specific wavelengths near 1,150 nm, 1,410 nm, and 1,927 nm, associated with water molecular states and liver function biomarkers (GPT, GOT, GGT), reinforcing the biological relevance of this approach.ConclusionsThese findings suggest that integrating NIRS and clinical data through machine learning enhances HCV diagnostic capabilities, offering a scalable and non-invasive alternative for early detection and risk assessment. |
| format | Article |
| id | doaj-art-be9e6ec903a3472389a21d11e5a4fcae |
| institution | OA Journals |
| issn | 2296-858X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Medicine |
| spelling | doaj-art-be9e6ec903a3472389a21d11e5a4fcae2025-08-20T02:31:13ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2025-06-011210.3389/fmed.2025.15964761596476Exploratory integration of near-infrared spectroscopy with clinical data: a machine learning approach for HCV detection in serum samplesEloy Pérez-Gómez0José Gómez1José Gómez2Jennifer Gonzalo3Sergio Salgüero4Daniel Riado5María Luisa Casas6María Luisa Gutiérrez7Elena Jaime8Enrique Pérez-Martínez9Rafael García-Carretero10Javier Ramos11Conrado Fernández-Rodríguez12Conrado Fernández-Rodríguez13Myriam Catalá14Myriam Catalá15Luca Martino16Óscar Barquero-Pérez17Department of Signal Theory and Communications, EIF, University Rey Juan Carlos, Fuenlabrada, SpainDepartment of Biology and Geology, Physics and Inorganic Chemistry, ESCET, University Rey Juan Carlos, Móstoles, SpainInstituto de Investigación en Cambio Global (IICG-URJC), Universidad Rey Juan Carlos, Móstoles, SpainDepartment of Biology and Geology, Physics and Inorganic Chemistry, ESCET, University Rey Juan Carlos, Móstoles, SpainService of Clinical Biochemistry, Hospital Universitario Fundación Alcorcón, Alcorcón, SpainService of Gastroenterology, Hospital Universitario Rey Juan Carlos, Fuenlabrada, SpainService of Clinical Biochemistry, Hospital Universitario Fundación Alcorcón, Alcorcón, SpainService of Gastroenterology, Hospital Universitario Fundación Alcorcón, Alcorcón, SpainService of Clinical Biochemistry, Hospital Universitario Fundación Alcorcón, Alcorcón, SpainDepartment of Biology and Geology, Physics and Inorganic Chemistry, ESCET, University Rey Juan Carlos, Móstoles, SpainHospital Universitario Mostoles, Móstoles, SpainDepartment of Signal Theory and Communications, EIF, University Rey Juan Carlos, Fuenlabrada, SpainService of Gastroenterology, Hospital Universitario Fundación Alcorcón, Alcorcón, SpainDepartment of Medical Specialties and Public Health, University Rey Juan Carlos, Alcorcón, Madrid, SpainDepartment of Biology and Geology, Physics and Inorganic Chemistry, ESCET, University Rey Juan Carlos, Móstoles, SpainInstituto de Investigación en Cambio Global (IICG-URJC), Universidad Rey Juan Carlos, Móstoles, SpainDipartimento di Economia e Impresa, Universita di Catania, Catania, ItaliaDepartment of Signal Theory and Communications, EIF, University Rey Juan Carlos, Fuenlabrada, SpainBackgroundManaging chronic viral infections like Hepatitis C virus (HCV) often requires expensive healthcare resources and highly qualified personnel, making efficient diagnostic methods essential. Despite remarkable therapeutic advancements for the treatment of HCV, several challenges remain, such as improved fast diagnostic procedures allowing universal screening.ObjectiveWe propose a novel approach that combines Near-Infrared Spectroscopy (NIRS) and clinical data with machine learning (ML) to improve Hepatitis C Virus (HCV) detection in serum samples.MethodsNIRS offers a fast, non-destructive, and residue-free alternative to traditional diagnostic methods, while ML models enable feature selection and predictive analysis. We applied L1-regularized Logistic Regression (L1-LR) to identify the most informative wavelengths for HCV detection within the 1,000–2,500 nm range, and then integrated these spectral features with routine clinical markers using a Random Forest (RF) model. Our dataset comprised 137 serum samples from 38 patients, each represented by a NIRS spectrum and clinical data from blood tests.ResultsAfter preprocessing with Standard Normal Variate (SNV) correction and downsampling, the best-performing RF model, which combined NIRS features and clinical data, achieved an accuracy of 72.2% and an AUC-ROC of 0.850, outperforming models using only clinical or spectral data. Feature importance analysis highlighted specific wavelengths near 1,150 nm, 1,410 nm, and 1,927 nm, associated with water molecular states and liver function biomarkers (GPT, GOT, GGT), reinforcing the biological relevance of this approach.ConclusionsThese findings suggest that integrating NIRS and clinical data through machine learning enhances HCV diagnostic capabilities, offering a scalable and non-invasive alternative for early detection and risk assessment.https://www.frontiersin.org/articles/10.3389/fmed.2025.1596476/fullNIRSHCVHepatitis Cmachine learningpermutation feature importance |
| spellingShingle | Eloy Pérez-Gómez José Gómez José Gómez Jennifer Gonzalo Sergio Salgüero Daniel Riado María Luisa Casas María Luisa Gutiérrez Elena Jaime Enrique Pérez-Martínez Rafael García-Carretero Javier Ramos Conrado Fernández-Rodríguez Conrado Fernández-Rodríguez Myriam Catalá Myriam Catalá Luca Martino Óscar Barquero-Pérez Exploratory integration of near-infrared spectroscopy with clinical data: a machine learning approach for HCV detection in serum samples Frontiers in Medicine NIRS HCV Hepatitis C machine learning permutation feature importance |
| title | Exploratory integration of near-infrared spectroscopy with clinical data: a machine learning approach for HCV detection in serum samples |
| title_full | Exploratory integration of near-infrared spectroscopy with clinical data: a machine learning approach for HCV detection in serum samples |
| title_fullStr | Exploratory integration of near-infrared spectroscopy with clinical data: a machine learning approach for HCV detection in serum samples |
| title_full_unstemmed | Exploratory integration of near-infrared spectroscopy with clinical data: a machine learning approach for HCV detection in serum samples |
| title_short | Exploratory integration of near-infrared spectroscopy with clinical data: a machine learning approach for HCV detection in serum samples |
| title_sort | exploratory integration of near infrared spectroscopy with clinical data a machine learning approach for hcv detection in serum samples |
| topic | NIRS HCV Hepatitis C machine learning permutation feature importance |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2025.1596476/full |
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