Academic Journal
Clinical Stress Level Prediction Using Metabolic Biomarkers and Genetic Algorithm–Based Machine Learning Models.
| Title: | Clinical Stress Level Prediction Using Metabolic Biomarkers and Genetic Algorithm–Based Machine Learning Models. |
|---|---|
| Authors: | Espino-Salinas, Carlos H., Mendoza-González, Ricardo, Luna-García, Huizilopoztli, Cepeda-Argüelles, Alejandra, Sánchez-Reyna, Ana G., Galván-Tejada, Carlos E., Soto Murillo, Manuel Alejandro, Martínez Acuña, Mónica Imelda, Martínez Esquivel, Rosa Adriana |
| Source: | Applied Sciences (2076-3417); Apr2026, Vol. 16 Issue 8, p3636, 22p |
| Subject Terms: | Biomarkers, Genetic algorithms, Psychological stress, Machine learning, Stress management, Feature selection |
| Abstract: | Psychological stress is a major public health problem associated with adverse outcomes in physical and mental health. This study proposes an approach to predicting clinical stress levels using metabolic and endocrine biomarkers combined with machine learning models based on genetic algorithms. Data were obtained from 87 university students, including measurements of glucose, insulin, and cortisol, as well as perceived stress scores assessed using the Perceived Stress Scale (PSS). Stress levels were categorized into low ( n = 5 ), moderate ( n = 22 ), and high ( n = 60 ) classes, reflecting an imbalanced dataset. Feature engineering and genetic algorithm–based selection identified glucose concentration, the insulin–glucose ratio, and the insulin–cortisol ratio as the most relevant features. These were used to train XGBoost and Elastic Net models, which were evaluated using leave-one-out cross-validation. The XGBoost model achieved the best performance, with an accuracy of 0.77 and strong predictive capability for high stress levels. The results demonstrate the usefulness of machine learning based on metabolic biomarkers as an objective tool for stress assessment in psychological and clinical research. [ABSTRACT FROM AUTHOR] |
| Copyright of Applied Sciences (2076-3417) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Complementary Index |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:edb&genre=article&issn=20763417&ISBN=&volume=16&issue=8&date=20260415&spage=3636&pages=3636-3657&title=Applied Sciences (2076-3417)&atitle=Clinical%20Stress%20Level%20Prediction%20Using%20Metabolic%20Biomarkers%20and%20Genetic%20Algorithm%E2%80%93Based%20Machine%20Learning%20Models.&aulast=Espino-Salinas%2C%20Carlos%20H.&id=DOI:10.3390/app16083636 Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
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| Header | DbId: edb DbLabel: Complementary Index An: 193440408 RelevancyScore: 1061 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1060.76000976563 |
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| Items | – Name: Title Label: Title Group: Ti Data: Clinical Stress Level Prediction Using Metabolic Biomarkers and Genetic Algorithm–Based Machine Learning Models. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Espino-Salinas%2C+Carlos+H%2E%22">Espino-Salinas, Carlos H.</searchLink><br /><searchLink fieldCode="AR" term="%22Mendoza-González%2C+Ricardo%22">Mendoza-González, Ricardo</searchLink><br /><searchLink fieldCode="AR" term="%22Luna-García%2C+Huizilopoztli%22">Luna-García, Huizilopoztli</searchLink><br /><searchLink fieldCode="AR" term="%22Cepeda-Argüelles%2C+Alejandra%22">Cepeda-Argüelles, Alejandra</searchLink><br /><searchLink fieldCode="AR" term="%22Sánchez-Reyna%2C+Ana+G%2E%22">Sánchez-Reyna, Ana G.</searchLink><br /><searchLink fieldCode="AR" term="%22Galván-Tejada%2C+Carlos+E%2E%22">Galván-Tejada, Carlos E.</searchLink><br /><searchLink fieldCode="AR" term="%22Soto+Murillo%2C+Manuel+Alejandro%22">Soto Murillo, Manuel Alejandro</searchLink><br /><searchLink fieldCode="AR" term="%22Martínez+Acuña%2C+Mónica+Imelda%22">Martínez Acuña, Mónica Imelda</searchLink><br /><searchLink fieldCode="AR" term="%22Martínez+Esquivel%2C+Rosa+Adriana%22">Martínez Esquivel, Rosa Adriana</searchLink> – Name: TitleSource Label: Source Group: Src Data: Applied Sciences (2076-3417); Apr2026, Vol. 16 Issue 8, p3636, 22p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Biomarkers%22">Biomarkers</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+algorithms%22">Genetic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Psychological+stress%22">Psychological stress</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Stress+management%22">Stress management</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Psychological stress is a major public health problem associated with adverse outcomes in physical and mental health. This study proposes an approach to predicting clinical stress levels using metabolic and endocrine biomarkers combined with machine learning models based on genetic algorithms. Data were obtained from 87 university students, including measurements of glucose, insulin, and cortisol, as well as perceived stress scores assessed using the Perceived Stress Scale (PSS). Stress levels were categorized into low ( n = 5 ), moderate ( n = 22 ), and high ( n = 60 ) classes, reflecting an imbalanced dataset. Feature engineering and genetic algorithm–based selection identified glucose concentration, the insulin–glucose ratio, and the insulin–cortisol ratio as the most relevant features. These were used to train XGBoost and Elastic Net models, which were evaluated using leave-one-out cross-validation. The XGBoost model achieved the best performance, with an accuracy of 0.77 and strong predictive capability for high stress levels. The results demonstrate the usefulness of machine learning based on metabolic biomarkers as an objective tool for stress assessment in psychological and clinical research. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Applied Sciences (2076-3417) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/app16083636 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 22 StartPage: 3636 Subjects: – SubjectFull: Biomarkers Type: general – SubjectFull: Genetic algorithms Type: general – SubjectFull: Psychological stress Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Stress management Type: general – SubjectFull: Feature selection Type: general Titles: – TitleFull: Clinical Stress Level Prediction Using Metabolic Biomarkers and Genetic Algorithm–Based Machine Learning Models. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Espino-Salinas, Carlos H. – PersonEntity: Name: NameFull: Mendoza-González, Ricardo – PersonEntity: Name: NameFull: Luna-García, Huizilopoztli – PersonEntity: Name: NameFull: Cepeda-Argüelles, Alejandra – PersonEntity: Name: NameFull: Sánchez-Reyna, Ana G. – PersonEntity: Name: NameFull: Galván-Tejada, Carlos E. – PersonEntity: Name: NameFull: Soto Murillo, Manuel Alejandro – PersonEntity: Name: NameFull: Martínez Acuña, Mónica Imelda – PersonEntity: Name: NameFull: Martínez Esquivel, Rosa Adriana IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 04 Text: Apr2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20763417 Numbering: – Type: volume Value: 16 – Type: issue Value: 8 Titles: – TitleFull: Applied Sciences (2076-3417) Type: main |
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