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Breast cancer diagnosis based on hybrid rule-based feature selection with deep learning algorithm

Breast cancer diagnosis based on hybrid rule-based feature selection with deep learning algorithm PurposeOne of the leading causes of death among women is breast cancer. However, it has been established that early diagnosis with accurate results can ensure the prolonged survival of patients even with the illness. Deep learning (DL) and expert systems have been proven beneficial and gaining popularity in breast cancer diagnosis because of their effective taxonomy and high diagnostic capability.MethodThis paper proposes a DL-based breast cancer model empowered with a rule-based hybrid feature selection mechanism to remove irrelevant features, thus proving to be a catalyst for improving diagnostic accuracy. The DL-based enabled feature selection helps in key attributes that are relevant to the diagnosis of breast cancer. The model has been tested utilizing the well-known Wisconsin Breast Cancer Dataset (WBCD) and validated through performance measures such as accuracy, sensitivity, specificity, F-score, and ROC curves.ResultsThe experimental results revealed that the DL-based enabled with feature selection performed excellently when compared with existing models on breast cancer using the same dataset. The findings show a greater diagnostic accuracy of 99.5% and detect five insightful features with a significant clue for better diagnosis.ConclusionThe proposed model can predict the presence of breast cancer by identifying the most relevant features in the diagnosis of breast cancer. The system looks promising when compared to other existing models for breast cancer. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Research on Biomedical Engineering Springer Journals

Breast cancer diagnosis based on hybrid rule-based feature selection with deep learning algorithm

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Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to The Brazilian Society of Biomedical Engineering 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
ISSN
2446-4732
eISSN
2446-4740
DOI
10.1007/s42600-022-00255-7
Publisher site
See Article on Publisher Site

Abstract

PurposeOne of the leading causes of death among women is breast cancer. However, it has been established that early diagnosis with accurate results can ensure the prolonged survival of patients even with the illness. Deep learning (DL) and expert systems have been proven beneficial and gaining popularity in breast cancer diagnosis because of their effective taxonomy and high diagnostic capability.MethodThis paper proposes a DL-based breast cancer model empowered with a rule-based hybrid feature selection mechanism to remove irrelevant features, thus proving to be a catalyst for improving diagnostic accuracy. The DL-based enabled feature selection helps in key attributes that are relevant to the diagnosis of breast cancer. The model has been tested utilizing the well-known Wisconsin Breast Cancer Dataset (WBCD) and validated through performance measures such as accuracy, sensitivity, specificity, F-score, and ROC curves.ResultsThe experimental results revealed that the DL-based enabled with feature selection performed excellently when compared with existing models on breast cancer using the same dataset. The findings show a greater diagnostic accuracy of 99.5% and detect five insightful features with a significant clue for better diagnosis.ConclusionThe proposed model can predict the presence of breast cancer by identifying the most relevant features in the diagnosis of breast cancer. The system looks promising when compared to other existing models for breast cancer.

Journal

Research on Biomedical Engineering – Springer Journals

Published: Mar 1, 2023

Keywords: Breast cancer; Diagnosis; Deep learning; Feature selection; Expert system

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