Simulación de sistema de tomografía por impedancia eléctrica sin contacto orientado a la detección de cáncer de próstata

Este proyecto propone la simulación de un sistema de tomografía por impedancia eléctrica (TIE) sin contacto que utiliza mediciones de propiedades eléctricas de tejidos para clasificar pacientes sanos, con hiperplasia y con cáncer de próstata, el sistema consta de varias etapas clave, incluida la sel...

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Autores:: Cuervo Lara, Sebastian Sneyder
Garcia Hidalgo, Manuel Alejandro

Tipo de recurso:: Trabajo de grado de pregrado

Fecha de publicación:: 2023

Institución:: Universidad Militar Nueva Granada

Repositorio:: Repositorio UMNG

Idioma:: spa

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network_acronym_str	UNIMILTAR2
network_name_str	Repositorio UMNG
repository_id_str
dc.title.spa.fl_str_mv	Simulación de sistema de tomografía por impedancia eléctrica sin contacto orientado a la detección de cáncer de próstata
dc.title.eng.fl_str_mv	Simulation of a non-contact electrical impedance tomography system for prostate cancer detection
title	Simulación de sistema de tomografía por impedancia eléctrica sin contacto orientado a la detección de cáncer de próstata
spellingShingle	Simulación de sistema de tomografía por impedancia eléctrica sin contacto orientado a la detección de cáncer de próstata TOMOGRAFIA POR IMPEDANCIA ELECTRICA - SIMULACION CANCER DE PROSTATA - DIAGNOSTICO - METODOS NO INVASIVOS IMAGEN MEDICA - PROCESAMIENTO DE SEÑALES BIOMEDICINA - APLICACIONES DE LA IMPEDANCIA ELECTRICA Sistema de tomografía por impedancia eléctrica Clasificación de cáncer de próstata Machine learning Seguridad del diagnóstico Validación y selección de modelos Electrical impedance tomography system Prostate cancer classification Machine learning Diagnosis safety Model validation and selection
title_short	Simulación de sistema de tomografía por impedancia eléctrica sin contacto orientado a la detección de cáncer de próstata
title_full	Simulación de sistema de tomografía por impedancia eléctrica sin contacto orientado a la detección de cáncer de próstata
title_fullStr	Simulación de sistema de tomografía por impedancia eléctrica sin contacto orientado a la detección de cáncer de próstata
title_full_unstemmed	Simulación de sistema de tomografía por impedancia eléctrica sin contacto orientado a la detección de cáncer de próstata
title_sort	Simulación de sistema de tomografía por impedancia eléctrica sin contacto orientado a la detección de cáncer de próstata
dc.creator.fl_str_mv	Cuervo Lara, Sebastian Sneyder Garcia Hidalgo, Manuel Alejandro
dc.contributor.advisor.none.fl_str_mv	Gómez Portilla, Jhon Andrés
dc.contributor.author.none.fl_str_mv	Cuervo Lara, Sebastian Sneyder Garcia Hidalgo, Manuel Alejandro
dc.contributor.other.none.fl_str_mv	Moreno Ortiz, Juan Pablo
dc.subject.lemb.spa.fl_str_mv	TOMOGRAFIA POR IMPEDANCIA ELECTRICA - SIMULACION CANCER DE PROSTATA - DIAGNOSTICO - METODOS NO INVASIVOS IMAGEN MEDICA - PROCESAMIENTO DE SEÑALES BIOMEDICINA - APLICACIONES DE LA IMPEDANCIA ELECTRICA
topic	TOMOGRAFIA POR IMPEDANCIA ELECTRICA - SIMULACION CANCER DE PROSTATA - DIAGNOSTICO - METODOS NO INVASIVOS IMAGEN MEDICA - PROCESAMIENTO DE SEÑALES BIOMEDICINA - APLICACIONES DE LA IMPEDANCIA ELECTRICA Sistema de tomografía por impedancia eléctrica Clasificación de cáncer de próstata Machine learning Seguridad del diagnóstico Validación y selección de modelos Electrical impedance tomography system Prostate cancer classification Machine learning Diagnosis safety Model validation and selection
dc.subject.proposal.spa.fl_str_mv	Sistema de tomografía por impedancia eléctrica Clasificación de cáncer de próstata Machine learning Seguridad del diagnóstico Validación y selección de modelos
dc.subject.proposal.eng.fl_str_mv	Electrical impedance tomography system Prostate cancer classification Machine learning Diagnosis safety Model validation and selection
description	Este proyecto propone la simulación de un sistema de tomografía por impedancia eléctrica (TIE) sin contacto que utiliza mediciones de propiedades eléctricas de tejidos para clasificar pacientes sanos, con hiperplasia y con cáncer de próstata, el sistema consta de varias etapas clave, incluida la selección de características anatómicas y bioeléctricas, diseño del sistema EIT, análisis exploratorio de datos e implementación de modelos de machine learning. El enfoque de machine learning da como resultados porcentajes de precisión del 85% para arboles de decisión, random forest y k-vecinos cercanos, considerando el sesgo para evitar el overfitting, asimismo se obtienen porcentajes altos para las demás métricas de calidad y un adecuado uso de la muestra demostrado por la validación cruzada.
publishDate	2023
dc.date.issued.none.fl_str_mv	2023-12-13
dc.date.accessioned.none.fl_str_mv	2025-04-01T14:13:13Z
dc.date.available.none.fl_str_mv	2025-04-01T14:13:13Z
dc.type.local.spa.fl_str_mv	Tesis/Trabajo de grado - Monografía - Pregrado
dc.type.driver.none.fl_str_mv	info:eu-repo/semantics/bachelorThesis
dc.type.coar.none.fl_str_mv	http://purl.org/coar/resource_type/c_7a1f
format	http://purl.org/coar/resource_type/c_7a1f
dc.identifier.uri.none.fl_str_mv	https://hdl.handle.net/10654/47157
dc.identifier.instname.spa.fl_str_mv	instname:Universidad Militar Nueva Granada
dc.identifier.reponame.spa.fl_str_mv	reponame:Repositorio Institucional Universidad Militar Nueva Granada
dc.identifier.repourl.none.fl_str_mv	repourl:https://repository.umng.edu.co
url	https://hdl.handle.net/10654/47157
identifier_str_mv	instname:Universidad Militar Nueva Granada reponame:Repositorio Institucional Universidad Militar Nueva Granada repourl:https://repository.umng.edu.co
dc.language.iso.none.fl_str_mv	spa
language	spa
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spelling	Gómez Portilla, Jhon AndrésCuervo Lara, Sebastian SneyderGarcia Hidalgo, Manuel AlejandroIngeniero BiomédicoMoreno Ortiz, Juan Pablo2025-04-01T14:13:13Z2025-04-01T14:13:13Z2023-12-13https://hdl.handle.net/10654/47157instname:Universidad Militar Nueva Granadareponame:Repositorio Institucional Universidad Militar Nueva Granadarepourl:https://repository.umng.edu.coEste proyecto propone la simulación de un sistema de tomografía por impedancia eléctrica (TIE) sin contacto que utiliza mediciones de propiedades eléctricas de tejidos para clasificar pacientes sanos, con hiperplasia y con cáncer de próstata, el sistema consta de varias etapas clave, incluida la selección de características anatómicas y bioeléctricas, diseño del sistema EIT, análisis exploratorio de datos e implementación de modelos de machine learning. El enfoque de machine learning da como resultados porcentajes de precisión del 85% para arboles de decisión, random forest y k-vecinos cercanos, considerando el sesgo para evitar el overfitting, asimismo se obtienen porcentajes altos para las demás métricas de calidad y un adecuado uso de la muestra demostrado por la validación cruzada.This project proposes the simulation of a non-contact electrical impedance tomography (EIT) system that uses electrical property measurements of tissues to classify healthy, hyperplasia and prostate cancer patients. The machine learning approach guarantees diagnostic feasibility, while ensuring greater safety and convenience for patients. The system consists of several key stages, including anatomical and bioelectrical feature selection, EIT system design, exploratory data analysis, machine learning model implementation, and meticulous validation of the models for selection.Contenido…………………………………………………………………………….4 Resumen ………………………………………………………………………………………….5 Lista de figuras ……………………………………………………………………………………8 Lista de tablas …………………………………………………………………………………...10 1. Introducción........................................................................................................... 11 2. Definición del problema ........................................................................................ 16 2.1 Pregunta de investigación.................................................................................16 2.2 Formulación del problema.................................................................................16 2.3 Planteamiento del problema .............................................................................18 2.4 Justificación del proyecto..................................................................................19 2.5 Objetivos del proyecto ......................................................................................21 2.5.1 Objetivo General ........................................................................................... 21 2.5.2 Objetivos Específicos .................................................................................... 21 2.6 Delimitación ......................................................................................................21 2.7 Variables...........................................................................................................22 2.7.1 Variable Dependiente.................................................................................... 22 2.7.2 Variables independientes .............................................................................. 22 2.7.3 Variables intervinientes ................................................................................. 22 2.8 Hipótesis...........................................................................................................23 3. Marco Referencial.................................................................................................. 24 3.1 Antecedentes del proyecto................................................................................24 3.1.1 Bioimpedancia en cáncer de próstata............................................................ 24 3.1.2 TIE en detección de cáncer de próstata ........................................................ 26 3.2 Marco Contextual..............................................................................................27 3.3 Marco teórico....................................................................................................27 3.3.1 Próstata......................................................................................................... 27 3.3.2 Tomografía por impedancia eléctrica (TIE).................................................... 28 3.3.3 Análisis exploratorio de datos........................................................................ 33 3.3.4 Clasificación por Machine Learning............................................................... 36 3.3.5 Selección de modelos ................................................................................... 43 3.4 Marco conceptual..............................................................................................48 4. Metodología............................................................................................................ 50 4.1 Tipo de estudio .................................................................................................50 4.2 Participantes o sujetos......................................................................................51 4.3 Herramientas, aparatos, materiales o instrumentos ..........................................51 4.4 Etapas del proyecto ..........................................................................................52 4.5 Consideraciones éticas .................................................................................... 52 4.6 Método de análisis e interpretación de los datos .............................................. 53 4.7 Recursos del proyecto...................................................................................... 53 4.8 Cronograma ..................................................................................................... 54 4.9 Resultados esperados...................................................................................... 54 4.10 Impacto del proyecto........................................................................................ 54 5. Implementación......................................................................................................56 5.1 Diseño de sistema de tomografía por impedancia eléctrica sin contacto .......... 56 5.2 Metodología de adquisición de datos ............................................................... 59 5.3 Análisis exploratorio de datos........................................................................... 66 5.4 Desarrollo de modelo de clasificación .............................................................. 70 5.5 Evaluación de los resultados............................................................................ 72 6. Resultados..............................................................................................................74 6.1 Visualización de modelos de machine learning ................................................ 74 6.2 Desarrollo y selección de modelos ................................................................... 86 6.3 Validación de modelos ..................................................................................... 90 6.4 Comparación del desempeño de los modelos.................................................101 7. Conclusiones........................................................................................................105 8. Agradecimientos ..................................................................................................107 9. Bibliografía ...........................................................................................................108Pregradoapplicaction/pdfspahttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 InternationalAcceso abiertohttp://purl.org/coar/access_right/c_abf2Simulación de sistema de tomografía por impedancia eléctrica sin contacto orientado a la detección de cáncer de próstataSimulation of a non-contact electrical impedance tomography system for prostate cancer detectionTOMOGRAFIA POR IMPEDANCIA ELECTRICA - SIMULACIONCANCER DE PROSTATA - DIAGNOSTICO - METODOS NO INVASIVOSIMAGEN MEDICA - PROCESAMIENTO DE SEÑALESBIOMEDICINA - APLICACIONES DE LA IMPEDANCIA ELECTRICASistema de tomografía por impedancia eléctricaClasificación de cáncer de próstataMachine learningSeguridad del diagnósticoValidación y selección de modelosElectrical impedance tomography systemProstate cancer classificationMachine learningDiagnosis safetyModel validation and selectionTesis/Trabajo de grado - Monografía - Pregradoinfo:eu-repo/semantics/bachelorThesishttp://purl.org/coar/resource_type/c_7a1fIngeniería BiomédicaFacultad de IngenieríaUniversidad Militar Nueva GranadaAmerican Cancer Society. 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The clinical application of electrical impedance technology in the detection of malignant neoplasms: a systematic review. Journal of Translational Medicine, 18(1). doi: 10.1186/s12967-020-02395-9Wan, Y., et al. (2012). Combined ultrasound and transrectal electrical impedance imaging of the prostate. In 2012 38th Annual Northeast Bioengineering Conference (NEBEC) (pp. 181–182). doi: 10.1109/NEBC.2012.6207023Yang, Y. (2017). An Advanced Digital Electrical Impedance Tomography System for Biomedical Imaging. The University of EdinburghDuongthipthewa, O., Uliss, P., Pattarasritanawong, P., Sukaimod, P., Ouypornkochagorn, T. (2020). Analysis of Current Patterns to Determine the Bladder Volume by Electrical Impedance Tomography (EIT). ACM International Conference Proceedings, 122–127. doi: 10.1145/3397391.3397433Luo, Y., et al. (2018). Non-invasive electrical impedance tomography for multi-scale detection of liver fat content. 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IFMBE Proceedings, 25(7), 399–402. https://doi.org/10.1007/978-3-642-03885-3_111Campus UMNGORIGINALCuervoLaraSebastianSneyder2023.pdfCuervoLaraSebastianSneyder2023.pdfTrabajo de grado - Tesisapplication/pdf3670178https://repository.umng.edu.co/bitstreams/8e675c8d-e030-4ce7-ab21-401aedff5d45/download5e58da9669bd0904330c01a4b86e3b4bMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-83420https://repository.umng.edu.co/bitstreams/46129a26-122e-4fa8-8d63-d380643f12da/downloada609d7e369577f685ce98c66b903b91bMD52THUMBNAILCuervoLaraSebastianSneyder2023.pdf.jpgCuervoLaraSebastianSneyder2023.pdf.jpgIM Thumbnailimage/jpeg5971https://repository.umng.edu.co/bitstreams/52c9829d-0a5a-43dc-8247-c16d1c00b9bf/downloaddf6b578f94614c46a14036a171b0cc54MD5310654/47157oai:repository.umng.edu.co:10654/471572025-04-02 03:00:56.919http://creativecommons.org/licenses/by-nc-nd/4.0/open.accesshttps://repository.umng.edu.coRepositorio Institucional 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