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Ensemble-based machine learning algorithms for classifying breast tissue based on electrical impedance spectroscopy

The initial identification of breast cancer and the prediction of its category have become a requirement in cancer research because they can simplify the subsequent clinical management of patients. The application of artificial intelligence techniques (e.g., machine learning and deep learning) in me...

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Main Authors: Rahman, Sam Matiur, Ali, Md. Asraf, Altwijri, Omar, Alqahtani, Mahdi, Ahmed, Nasim, Ahamed, Nizam Uddin
Format: Conference or Workshop Item
Language:English
Published: Springer Verlag 2020
Subjects:
TJ Mechanical engineering and machinery
TS Manufactures
Online Access:http://umpir.ump.edu.my/id/eprint/25634/1/Ensemble-based%20machine%20learning%20algorithms%20for%20classifying%20breast%20.pdf
http://umpir.ump.edu.my/id/eprint/25634/
https://doi.org/10.1007/978-3-030-20454-9_26
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spelling my.ump.umpir.256342019-12-13T07:19:19Z http://umpir.ump.edu.my/id/eprint/25634/ Ensemble-based machine learning algorithms for classifying breast tissue based on electrical impedance spectroscopy Rahman, Sam Matiur Ali, Md. Asraf Altwijri, Omar Alqahtani, Mahdi Ahmed, Nasim Ahamed, Nizam Uddin TJ Mechanical engineering and machinery TS Manufactures The initial identification of breast cancer and the prediction of its category have become a requirement in cancer research because they can simplify the subsequent clinical management of patients. The application of artificial intelligence techniques (e.g., machine learning and deep learning) in medical science is becoming increasingly important for intelligently transforming all available information into valuable knowledge. Therefore, we aimed to classify six classes of freshly excised tissues from a set of electrical impedance measurement variables using five ensemble-based machine learning (ML) algorithms, namely, the random forest (RF), extremely randomized trees (ERT), decision tree (DT), gradient boosting tree (GBT) and AdaBoost (Adaptive Boosting) (ADB) algorithms, which can be subcategorized as bagging and boosting methods. In addition, the ranked order of the variables based on their importance differed across the ML algorithms. The results demonstrated that the three bagging ensemble ML algorithms, namely, RF ERT and DT, yielded better classification accuracies (78–86%) compared with the two boosting algorithms, GBT and ADB (60–75%). We hope that these our results would help improve the classification of breast tissue to allow the early prediction of cancer susceptibility. Springer Verlag 2020 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/25634/1/Ensemble-based%20machine%20learning%20algorithms%20for%20classifying%20breast%20.pdf Rahman, Sam Matiur and Ali, Md. Asraf and Altwijri, Omar and Alqahtani, Mahdi and Ahmed, Nasim and Ahamed, Nizam Uddin (2020) Ensemble-based machine learning algorithms for classifying breast tissue based on electrical impedance spectroscopy. In: International Conference on Applied Human Factors and Ergonomics : AHFE 2019, 24 - 28 July 2019 , Washington D.C., United States. pp. 260-266., 965. ISSN 2194-5357 ISBN 978-3-030-20453-2 (Print); 978-3-030-20454-9 (Online) https://doi.org/10.1007/978-3-030-20454-9_26
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic TJ Mechanical engineering and machinery
TS Manufactures
spellingShingle TJ Mechanical engineering and machinery
TS Manufactures
Rahman, Sam Matiur
Ali, Md. Asraf
Altwijri, Omar
Alqahtani, Mahdi
Ahmed, Nasim
Ahamed, Nizam Uddin
Ensemble-based machine learning algorithms for classifying breast tissue based on electrical impedance spectroscopy
description The initial identification of breast cancer and the prediction of its category have become a requirement in cancer research because they can simplify the subsequent clinical management of patients. The application of artificial intelligence techniques (e.g., machine learning and deep learning) in medical science is becoming increasingly important for intelligently transforming all available information into valuable knowledge. Therefore, we aimed to classify six classes of freshly excised tissues from a set of electrical impedance measurement variables using five ensemble-based machine learning (ML) algorithms, namely, the random forest (RF), extremely randomized trees (ERT), decision tree (DT), gradient boosting tree (GBT) and AdaBoost (Adaptive Boosting) (ADB) algorithms, which can be subcategorized as bagging and boosting methods. In addition, the ranked order of the variables based on their importance differed across the ML algorithms. The results demonstrated that the three bagging ensemble ML algorithms, namely, RF ERT and DT, yielded better classification accuracies (78–86%) compared with the two boosting algorithms, GBT and ADB (60–75%). We hope that these our results would help improve the classification of breast tissue to allow the early prediction of cancer susceptibility.
format Conference or Workshop Item
author Rahman, Sam Matiur
Ali, Md. Asraf
Altwijri, Omar
Alqahtani, Mahdi
Ahmed, Nasim
Ahamed, Nizam Uddin
author_facet Rahman, Sam Matiur
Ali, Md. Asraf
Altwijri, Omar
Alqahtani, Mahdi
Ahmed, Nasim
Ahamed, Nizam Uddin
author_sort Rahman, Sam Matiur
title Ensemble-based machine learning algorithms for classifying breast tissue based on electrical impedance spectroscopy
title_short Ensemble-based machine learning algorithms for classifying breast tissue based on electrical impedance spectroscopy
title_full Ensemble-based machine learning algorithms for classifying breast tissue based on electrical impedance spectroscopy
title_fullStr Ensemble-based machine learning algorithms for classifying breast tissue based on electrical impedance spectroscopy
title_full_unstemmed Ensemble-based machine learning algorithms for classifying breast tissue based on electrical impedance spectroscopy
title_sort ensemble-based machine learning algorithms for classifying breast tissue based on electrical impedance spectroscopy
publisher Springer Verlag
publishDate 2020
url http://umpir.ump.edu.my/id/eprint/25634/1/Ensemble-based%20machine%20learning%20algorithms%20for%20classifying%20breast%20.pdf
http://umpir.ump.edu.my/id/eprint/25634/
https://doi.org/10.1007/978-3-030-20454-9_26
_version_ 1654960220968845312
score 13.211869

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