Evaluation of ANN and SVM for the classification and prediction of patients with diabetic neuropathy

Autores
González Rubio, Tahimy; Salgado Castillo, Antonio
Año de publicación
2012
Idioma
español castellano
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Diabetic neuropathy is a disease that affects a large proportion of the world population, so that its prevention and early detection is of vital importance at the present time. In this paper we evaluate an ANN and SVM designed using MatLab 7.9.0.529 for the classification and prediction of patients with diabetic neuropathy, using Pulse Waves Sequences of Blood Volume. Efficiency was evaluated taking into account the algorithms and training time as well as effectiveness in classification and prediction. Considering 40 cases in the process of learning and 18 in the validation, the best classification results were obtained with the ANN for an 88.88% effective with the Gradient descent learning algorithm with adaptive learning rate, and the SVM was obtained 72.22% success rate using the Quadratic programming algorithm. In predicting both methods were 100% effective.
Eje: Workshop Procesamiento de señales y sistemas de tiempo real (WPSTR)
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
Real time
Signal processing
SVM
ANN
Pulse Waves
Diabetic Neuropathy
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/23819
Acceder
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repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Evaluation of ANN and SVM for the classification and prediction of patients with diabetic neuropathyGonzález Rubio, TahimySalgado Castillo, AntonioCiencias InformáticasReal timeSignal processingSVMANNPulse WavesDiabetic NeuropathyDiabetic neuropathy is a disease that affects a large proportion of the world population, so that its prevention and early detection is of vital importance at the present time. In this paper we evaluate an ANN and SVM designed using MatLab 7.9.0.529 for the classification and prediction of patients with diabetic neuropathy, using Pulse Waves Sequences of Blood Volume. Efficiency was evaluated taking into account the algorithms and training time as well as effectiveness in classification and prediction. Considering 40 cases in the process of learning and 18 in the validation, the best classification results were obtained with the ANN for an 88.88% effective with the Gradient descent learning algorithm with adaptive learning rate, and the SVM was obtained 72.22% success rate using the Quadratic programming algorithm. In predicting both methods were 100% effective.Eje: Workshop Procesamiento de señales y sistemas de tiempo real (WPSTR)Red de Universidades con Carreras en Informática (RedUNCI)2012-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/23819spainfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T10:55:36Zoai:sedici.unlp.edu.ar:10915/23819Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:55:36.571SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Evaluation of ANN and SVM for the classification and prediction of patients with diabetic neuropathy
title Evaluation of ANN and SVM for the classification and prediction of patients with diabetic neuropathy
spellingShingle Evaluation of ANN and SVM for the classification and prediction of patients with diabetic neuropathy
González Rubio, Tahimy
Ciencias Informáticas
Real time
Signal processing
SVM
ANN
Pulse Waves
Diabetic Neuropathy
title_short Evaluation of ANN and SVM for the classification and prediction of patients with diabetic neuropathy
title_full Evaluation of ANN and SVM for the classification and prediction of patients with diabetic neuropathy
title_fullStr Evaluation of ANN and SVM for the classification and prediction of patients with diabetic neuropathy
title_full_unstemmed Evaluation of ANN and SVM for the classification and prediction of patients with diabetic neuropathy
title_sort Evaluation of ANN and SVM for the classification and prediction of patients with diabetic neuropathy
dc.creator.none.fl_str_mv González Rubio, Tahimy
Salgado Castillo, Antonio
author González Rubio, Tahimy
author_facet González Rubio, Tahimy
Salgado Castillo, Antonio
author_role author
author2 Salgado Castillo, Antonio
author2_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
Real time
Signal processing
SVM
ANN
Pulse Waves
Diabetic Neuropathy
topic Ciencias Informáticas
Real time
Signal processing
SVM
ANN
Pulse Waves
Diabetic Neuropathy
dc.description.none.fl_txt_mv Diabetic neuropathy is a disease that affects a large proportion of the world population, so that its prevention and early detection is of vital importance at the present time. In this paper we evaluate an ANN and SVM designed using MatLab 7.9.0.529 for the classification and prediction of patients with diabetic neuropathy, using Pulse Waves Sequences of Blood Volume. Efficiency was evaluated taking into account the algorithms and training time as well as effectiveness in classification and prediction. Considering 40 cases in the process of learning and 18 in the validation, the best classification results were obtained with the ANN for an 88.88% effective with the Gradient descent learning algorithm with adaptive learning rate, and the SVM was obtained 72.22% success rate using the Quadratic programming algorithm. In predicting both methods were 100% effective.
Eje: Workshop Procesamiento de señales y sistemas de tiempo real (WPSTR)
Red de Universidades con Carreras en Informática (RedUNCI)
description Diabetic neuropathy is a disease that affects a large proportion of the world population, so that its prevention and early detection is of vital importance at the present time. In this paper we evaluate an ANN and SVM designed using MatLab 7.9.0.529 for the classification and prediction of patients with diabetic neuropathy, using Pulse Waves Sequences of Blood Volume. Efficiency was evaluated taking into account the algorithms and training time as well as effectiveness in classification and prediction. Considering 40 cases in the process of learning and 18 in the validation, the best classification results were obtained with the ANN for an 88.88% effective with the Gradient descent learning algorithm with adaptive learning rate, and the SVM was obtained 72.22% success rate using the Quadratic programming algorithm. In predicting both methods were 100% effective.
publishDate 2012
dc.date.none.fl_str_mv 2012-10
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info:eu-repo/semantics/publishedVersion
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url http://sedici.unlp.edu.ar/handle/10915/23819
dc.language.none.fl_str_mv spa
language spa
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
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Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
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repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
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