Machine Learning and Soft Sensors of Electronic Nose and Tongue Type for Cancer Detection

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dc.creatorGarcía-García, Laura M.
dc.creatorVallejo, Marcela
dc.creatorDelgado-Trejos, Edilson
dc.date2025-08-22
dc.date.accessioned2025-10-01T23:53:16Z
dc.descriptionCancer has high incidence and mortality rates worldwide and early diagnosis significantly improves survival outcomes. Consequently, there is a growing interest in non-invasive and cost-effective diagnostic tools, such as soft sensors for the analysis of Volatile Organic Compounds (VOCs), which can serve as biomarkers for the disease. This article aimed to present a comprehensive review on the use of electronic noses and tongues as soft sensors for cancer detection, along with data processing through machine learning algorithms. A qualitative methodology was employed, based on a literature review of databases including ScienceDirect, IEEEXplore, Sage Journals, and Scopus, resulting in the selection of 54 relevant articles published between 2010 and 2024. The articles were selected using a process aligned with the PRISMA methodology. The findings highlight the application of soft sensors for the detection of lungs, prostate, bladder, breast, ovarian, colorectal, gastric cancers, and oral cavity conditions, using samples such as urine, exhaled breath, saliva, and blood. The discussion addresses comparative analyses of representation and decision-making techniques, as well as emerging trends, challenges, and research opportunities in the field. The study concludes that integrating soft metrology with soft sensors and machine learning enables the accurate measurement of cancer biomarkers from biological substances, achieving detection accuracies of approximately 90%. However, significant research challenges and opportunities related to system architecture optimization remain to enhance reliability.en-US
dc.descriptionEl cáncer tiene alta incidencia y mortalidad a nivel mundial, y un diagnóstico temprano mejora significativamente la supervivencia. Por ello, se buscan herramientas no invasivas y económicas, como los soft sensores, para analizar Compuestos Orgánicos Volátiles (COV) que pueden actuar como biomarcadores de la enfermedad. Este artículo tuvo como objetivo revisar el estado del arte sobre el uso de narices y lenguas electrónicas como soft sensores para la detección de cáncer, junto con el procesamiento de datos mediante máquinas de aprendizaje. Se empleó una metodología cualitativa basada en la revisión de literatura científica publicada en bases de datos como ScienceDirect, IEEEXplore, Sage Journals y Scopus. Se seleccionaron 54 artículos relevantes, a partir de un proceso basado en la metodología PRISMA, publicados entre 2010 y 2024. Los resultados revelaron el uso de soft sensores para detectar cáncer de pulmón, próstata, vejiga, mama, ovario, colon, estómago y cavidad bucal, utilizando muestras como orina, aliento, saliva y sangre. La discusión incluyó comparaciones entre técnicas de análisis y decisiones, además se destacan tendencias, desafíos y oportunidades de investigación en el área. Se concluye que combinar la soft metrología, los soft sensores y el aprendizaje automático permite detectar biomarcadores del cáncer con una precisión alrededor del 90 %. No obstante, aún existen retos y oportunidades de investigación para optimizar las arquitecturas y lograr sistemas más confiables.es-ES
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dc.identifierhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/3296
dc.identifier10.22430/22565337.3296
dc.identifier.urihttps://hdl.handle.net/20.500.12622/7935
dc.languagespa
dc.publisherInstituto Tecnológico Metropolitano (ITM)es-ES
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/3296/3720
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/3296/3786
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/3296/3787
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dc.rightsDerechos de autor 2025 TecnoLógicases-ES
dc.rightshttps://creativecommons.org/licenses/by-nc-sa/4.0es-ES
dc.sourceTecnoLógicas; Vol. 28 No. 63 (2025); e3296en-US
dc.sourceTecnoLógicas; Vol. 28 Núm. 63 (2025); e3296es-ES
dc.source2256-5337
dc.source0123-7799
dc.subjectcompuestos orgánicos volátileses-ES
dc.subjectdiagnóstico no invasivoes-ES
dc.subjectespacio de representaciónes-ES
dc.subjectsoft metrologíaes-ES
dc.subjecttécnicas de aprendizajees-ES
dc.subjectvolatile organic compoundsen-US
dc.subjectnon-invasive diagnosisen-US
dc.subjectfeature spaceen-US
dc.subjectsoft metrologyen-US
dc.subjectlearning techniquesen-US
dc.titleMachine Learning and Soft Sensors of Electronic Nose and Tongue Type for Cancer Detectionen-US
dc.titleMáquinas de aprendizaje y soft sensores de tipo nariz y lengua electrónica para la detección de cánceres-ES
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typeReview Articleen-US
dc.typeArtículos de revisiónes-ES

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