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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2021
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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%) is better than using all available features (38%) and the medical records (33%). 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) |
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Electro-impedance conductivity machine learning mammography MEIK risk factor Electrical engineering. Electronics. Nuclear engineering TK1-9971 |
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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 |
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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%) is better than using all available features (38%) and the medical records (33%). 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 |
work_keys_str_mv |
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1718419824479043584 |