Machine Learning for the Analysis of Conductivity From Mono Frequency Electrical Impedance Mammography as a Breast Cancer Risk Factor

Computational approaches have been used for analyzing risk factors together with conventional mammograms for breast cancer detection. Currently, other screening methods like electro-impedance mammography are available. Notwithstanding, as far as we know there is not related work evaluating the role...

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Autores principales: Rosario Lissiet Romero Coripuna, Delia Irazu Hernandez Farias, Blanca Olivia Murillo Ortiz, Luis Carlos Padierna, Teodoro Cordova Fraga
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Lenguaje:EN
Publicado: IEEE 2021
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Acceso en línea:https://doaj.org/article/26b34056c76a48e2ade0d0b1d5d5e242
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spelling oai:doaj.org-article:26b34056c76a48e2ade0d0b1d5d5e2422021-11-20T00:02:09ZMachine Learning for the Analysis of Conductivity From Mono Frequency Electrical Impedance Mammography as a Breast Cancer Risk Factor2169-353610.1109/ACCESS.2021.3122948https://doaj.org/article/26b34056c76a48e2ade0d0b1d5d5e2422021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9585581/https://doaj.org/toc/2169-3536Computational approaches have been used for analyzing risk factors together with conventional mammograms for breast cancer detection. Currently, other screening methods like electro-impedance mammography are available. Notwithstanding, as far as we know there is not related work evaluating the role of electrical-conductivity index of the mammary gland as a quantitative factor for early detection of breast cancer. This paper aims to demonstrate the importance of including breast conductivity index as a quantitative local risk-factor by analyzing a dataset of Mexican patients from a machine learning perspective. There are 12 attributes distributed into two groups: <italic>electrical-conductivity</italic> (3) and <italic>medical records</italic> (9). According to the obtained results with unsupervised methods, the performance in terms of accuracy of using only electrical-conductivity (43&#x0025;) is better than using all available features (38&#x0025;) and the medical records (33&#x0025;). On the other hand, we identified that SVM achieves higher results in comparison with other algorithms when only the electrical-features are used. The obtained results demonstrate the important role of conductivity index as a quantitative local risk-factor for being considered in screening processes. Besides, it emerges as an important aspect to be included in the development of automatic tools for experts to perform breast cancer diagnosis.Rosario Lissiet Romero CoripunaDelia Irazu Hernandez FariasBlanca Olivia Murillo OrtizLuis Carlos PadiernaTeodoro Cordova FragaIEEEarticleElectro-impedanceconductivitymachine learningmammography MEIKrisk factorElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 152397-152407 (2021)
institution DOAJ
collection DOAJ
language EN
topic Electro-impedance
conductivity
machine learning
mammography MEIK
risk factor
Electrical engineering. Electronics. Nuclear engineering
TK1-9971
spellingShingle Electro-impedance
conductivity
machine learning
mammography MEIK
risk factor
Electrical engineering. Electronics. Nuclear engineering
TK1-9971
Rosario Lissiet Romero Coripuna
Delia Irazu Hernandez Farias
Blanca Olivia Murillo Ortiz
Luis Carlos Padierna
Teodoro Cordova Fraga
Machine Learning for the Analysis of Conductivity From Mono Frequency Electrical Impedance Mammography as a Breast Cancer Risk Factor
description Computational approaches have been used for analyzing risk factors together with conventional mammograms for breast cancer detection. Currently, other screening methods like electro-impedance mammography are available. Notwithstanding, as far as we know there is not related work evaluating the role of electrical-conductivity index of the mammary gland as a quantitative factor for early detection of breast cancer. This paper aims to demonstrate the importance of including breast conductivity index as a quantitative local risk-factor by analyzing a dataset of Mexican patients from a machine learning perspective. There are 12 attributes distributed into two groups: <italic>electrical-conductivity</italic> (3) and <italic>medical records</italic> (9). According to the obtained results with unsupervised methods, the performance in terms of accuracy of using only electrical-conductivity (43&#x0025;) is better than using all available features (38&#x0025;) and the medical records (33&#x0025;). On the other hand, we identified that SVM achieves higher results in comparison with other algorithms when only the electrical-features are used. The obtained results demonstrate the important role of conductivity index as a quantitative local risk-factor for being considered in screening processes. Besides, it emerges as an important aspect to be included in the development of automatic tools for experts to perform breast cancer diagnosis.
format article
author Rosario Lissiet Romero Coripuna
Delia Irazu Hernandez Farias
Blanca Olivia Murillo Ortiz
Luis Carlos Padierna
Teodoro Cordova Fraga
author_facet Rosario Lissiet Romero Coripuna
Delia Irazu Hernandez Farias
Blanca Olivia Murillo Ortiz
Luis Carlos Padierna
Teodoro Cordova Fraga
author_sort Rosario Lissiet Romero Coripuna
title Machine Learning for the Analysis of Conductivity From Mono Frequency Electrical Impedance Mammography as a Breast Cancer Risk Factor
title_short Machine Learning for the Analysis of Conductivity From Mono Frequency Electrical Impedance Mammography as a Breast Cancer Risk Factor
title_full Machine Learning for the Analysis of Conductivity From Mono Frequency Electrical Impedance Mammography as a Breast Cancer Risk Factor
title_fullStr Machine Learning for the Analysis of Conductivity From Mono Frequency Electrical Impedance Mammography as a Breast Cancer Risk Factor
title_full_unstemmed Machine Learning for the Analysis of Conductivity From Mono Frequency Electrical Impedance Mammography as a Breast Cancer Risk Factor
title_sort machine learning for the analysis of conductivity from mono frequency electrical impedance mammography as a breast cancer risk factor
publisher IEEE
publishDate 2021
url https://doaj.org/article/26b34056c76a48e2ade0d0b1d5d5e242
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