Breast cancer recurrence prediction with ensemble methods and cost-sensitive learning

Breast cancer is one of the most common cancers in women all over the world. Due to the improvement of medical treatments, most of the breast cancer patients would be in remission. However, the patients have to face the next challenge, the recurrence of breast cancer which may cause more severe effe...

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Autores principales:	Yang Pei-Tse, Wu Wen-Shuo, Wu Chia-Chun, Shih Yi-Nuo, Hsieh Chung-Ho, Hsu Jia-Lien
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2021
Materias:	recurrent breast cancer machine learning classification adaboost cost-sensitive method Medicine R
Acceso en línea:	https://doaj.org/article/b3caf70b408442378144d6d813871a4e
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spelling	oai:doaj.org-article:b3caf70b408442378144d6d813871a4e2021-12-05T14:10:54ZBreast cancer recurrence prediction with ensemble methods and cost-sensitive learning2391-546310.1515/med-2021-0282https://doaj.org/article/b3caf70b408442378144d6d813871a4e2021-05-01T00:00:00Zhttps://doi.org/10.1515/med-2021-0282https://doaj.org/toc/2391-5463Breast cancer is one of the most common cancers in women all over the world. Due to the improvement of medical treatments, most of the breast cancer patients would be in remission. However, the patients have to face the next challenge, the recurrence of breast cancer which may cause more severe effects, and even death. The prediction of breast cancer recurrence is crucial for reducing mortality. This paper proposes a prediction model for the recurrence of breast cancer based on clinical nominal and numeric features. In this study, our data consist of 1,061 patients from Breast Cancer Registry from Shin Kong Wu Ho-Su Memorial Hospital between 2011 and 2016, in which 37 records are denoted as breast cancer recurrence. Each record has 85 features. Our approach consists of three stages. First, we perform data preprocessing and feature selection techniques to consolidate the dataset. Among all features, six features are identified for further processing in the following stages. Next, we apply resampling techniques to resolve the issue of class imbalance. Finally, we construct two classifiers, AdaBoost and cost-sensitive learning, to predict the risk of recurrence and carry out the performance evaluation in three-fold cross-validation. By applying the AdaBoost method, we achieve accuracy of 0.973 and sensitivity of 0.675. By combining the AdaBoost and cost-sensitive method of our model, we achieve a reasonable accuracy of 0.468 and substantially high sensitivity of 0.947 which guarantee almost no false dismissal. Our model can be used as a supporting tool in the setting and evaluation of the follow-up visit for early intervention and more advanced treatments to lower cancer mortality.Yang Pei-TseWu Wen-ShuoWu Chia-ChunShih Yi-NuoHsieh Chung-HoHsu Jia-LienDe Gruyterarticlerecurrent breast cancermachine learningclassificationadaboostcost-sensitive methodMedicineRENOpen Medicine, Vol 16, Iss 1, Pp 754-768 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	recurrent breast cancer machine learning classification adaboost cost-sensitive method Medicine R
spellingShingle	recurrent breast cancer machine learning classification adaboost cost-sensitive method Medicine R Yang Pei-Tse Wu Wen-Shuo Wu Chia-Chun Shih Yi-Nuo Hsieh Chung-Ho Hsu Jia-Lien Breast cancer recurrence prediction with ensemble methods and cost-sensitive learning
description	Breast cancer is one of the most common cancers in women all over the world. Due to the improvement of medical treatments, most of the breast cancer patients would be in remission. However, the patients have to face the next challenge, the recurrence of breast cancer which may cause more severe effects, and even death. The prediction of breast cancer recurrence is crucial for reducing mortality. This paper proposes a prediction model for the recurrence of breast cancer based on clinical nominal and numeric features. In this study, our data consist of 1,061 patients from Breast Cancer Registry from Shin Kong Wu Ho-Su Memorial Hospital between 2011 and 2016, in which 37 records are denoted as breast cancer recurrence. Each record has 85 features. Our approach consists of three stages. First, we perform data preprocessing and feature selection techniques to consolidate the dataset. Among all features, six features are identified for further processing in the following stages. Next, we apply resampling techniques to resolve the issue of class imbalance. Finally, we construct two classifiers, AdaBoost and cost-sensitive learning, to predict the risk of recurrence and carry out the performance evaluation in three-fold cross-validation. By applying the AdaBoost method, we achieve accuracy of 0.973 and sensitivity of 0.675. By combining the AdaBoost and cost-sensitive method of our model, we achieve a reasonable accuracy of 0.468 and substantially high sensitivity of 0.947 which guarantee almost no false dismissal. Our model can be used as a supporting tool in the setting and evaluation of the follow-up visit for early intervention and more advanced treatments to lower cancer mortality.
format	article
author	Yang Pei-Tse Wu Wen-Shuo Wu Chia-Chun Shih Yi-Nuo Hsieh Chung-Ho Hsu Jia-Lien
author_facet	Yang Pei-Tse Wu Wen-Shuo Wu Chia-Chun Shih Yi-Nuo Hsieh Chung-Ho Hsu Jia-Lien
author_sort	Yang Pei-Tse
title	Breast cancer recurrence prediction with ensemble methods and cost-sensitive learning
title_short	Breast cancer recurrence prediction with ensemble methods and cost-sensitive learning
title_full	Breast cancer recurrence prediction with ensemble methods and cost-sensitive learning
title_fullStr	Breast cancer recurrence prediction with ensemble methods and cost-sensitive learning
title_full_unstemmed	Breast cancer recurrence prediction with ensemble methods and cost-sensitive learning
title_sort	breast cancer recurrence prediction with ensemble methods and cost-sensitive learning
publisher	De Gruyter
publishDate	2021
url	https://doaj.org/article/b3caf70b408442378144d6d813871a4e
work_keys_str_mv	AT yangpeitse breastcancerrecurrencepredictionwithensemblemethodsandcostsensitivelearning AT wuwenshuo breastcancerrecurrencepredictionwithensemblemethodsandcostsensitivelearning AT wuchiachun breastcancerrecurrencepredictionwithensemblemethodsandcostsensitivelearning AT shihyinuo breastcancerrecurrencepredictionwithensemblemethodsandcostsensitivelearning AT hsiehchungho breastcancerrecurrencepredictionwithensemblemethodsandcostsensitivelearning AT hsujialien breastcancerrecurrencepredictionwithensemblemethodsandcostsensitivelearning
_version_	1718371623314128896

Breast cancer recurrence prediction with ensemble methods and cost-sensitive learning

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