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

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)

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Abstract

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.

Item Type: Conference or Workshop Item (Lecture)
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Breast tissues; Machine learning; Ensemble learning; Classification; Electrical impedence
Subjects: T Technology > TJ Mechanical engineering and machinery
T Technology > TS Manufactures
Faculty/Division: Faculty of Manufacturing Engineering
Depositing User: Mrs Norsaini Abdul Samat
Date Deposited: 13 Dec 2019 07:19
Last Modified: 13 Dec 2019 07:19
URI: http://umpir.ump.edu.my/id/eprint/25634
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