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Artificial Intelligence: Advantages in Diagnostic Procedures

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 457

Special Issue Editors


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Guest Editor
Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Mexico
Interests: ambient intelligence; signal processing; biomedical engineering; context-aware computing; bioinformatics and game development
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Laboratorio de Inmunotoxicología, Unidad Académica de Ciencias Químicas, Universidad Autónoma de Zacatecas, Campus UAZ Siglo XXI, Carretera Zacatecas-Guadalajara km 6, Col. Ejido La Escondida, Zacatecas 98160, Mexico
Interests: immunology; toxicology; data science; clinical diagnosis; biomedical engineering

Special Issue Information

Dear Colleagues,

Artificial intelligence is revolutionizing diagnostic procedures, enabling the production of faster, more accurate, and reproducible results across a wide range of medical fields. This Special Issue aims to highlight novel methodologies and AI applications in the diagnosis of complex, multifactorial diseases through advances in machine learning, deep learning, explainable AI, and GPT techniques. We welcome contributions that present innovations in clinical data analysis and discuss the integration of multimodal sources such as imaging, biosignals, genomics, and wearables to develop personalized diagnostics. This Special Issue emphasizes reproducibility, the ethical use of AI, and improvements in public health outcomes and seeks to explore new diagnostic frameworks, address current limitations in clinical practice, and propose cutting-edge solutions.

Dr. Carlos Eric Galván-Tejada
Dr. Irma Gonzalez-Curiel
Guest Editors

Manuscript Submission Information

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Keywords

  • artificial intelligence
  • diagnostic systems
  • machine learning
  • deep learning
  • biomedical signal processing
  • medical imaging
  • bioinformatics
  • multimodal diagnostics
  • clinical decision support systems
  • personalized medicine

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Published Papers (1 paper)

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Research

22 pages, 1795 KB  
Article
Clinical Stress Level Prediction Using Metabolic Biomarkers and Genetic Algorithm–Based Machine Learning Models
by Carlos H. Espino-Salinas, Ricardo Mendoza-González, Huizilopoztli Luna-García, Alejandra Cepeda-Argüelles, Ana G. Sánchez-Reyna, Carlos E. Galván-Tejada, Manuel Alejandro Soto Murillo, Mónica Imelda Martínez Acuña and Rosa Adriana Martínez Esquivel
Appl. Sci. 2026, 16(8), 3636; https://doi.org/10.3390/app16083636 - 8 Apr 2026
Viewed by 210
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Artificial Intelligence: Advantages in Diagnostic Procedures)
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