An artificial immune system with bootstrap sampling for the diagnosis of recurrent endometrial cancers

Endometrial cancer is one of the most common gynecological malignancies in developed countries. The prevention of the recurrence of endometrial cancer has always been a clinical challenge. Endometrial cancer is asymptomatic in the early stage, and there remains a lack of time-series correlation patt...

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Autores principales:	Chang Chih-Yen, Lu Yen-Chiao (Angel), Ting Wen-Chien, Shen Tsu-Wang (David), Peng Wen-Chen
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2021
Materias:	artificial immune systems bootstrap sampling biomimetic intelligence recurrent endometrial cancers Medicine R
Acceso en línea:	https://doaj.org/article/75de332d8e204106861f52ad59daec38
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spelling	oai:doaj.org-article:75de332d8e204106861f52ad59daec382021-12-05T14:10:54ZAn artificial immune system with bootstrap sampling for the diagnosis of recurrent endometrial cancers2391-546310.1515/med-2021-0226https://doaj.org/article/75de332d8e204106861f52ad59daec382021-01-01T00:00:00Zhttps://doi.org/10.1515/med-2021-0226https://doaj.org/toc/2391-5463Endometrial cancer is one of the most common gynecological malignancies in developed countries. The prevention of the recurrence of endometrial cancer has always been a clinical challenge. Endometrial cancer is asymptomatic in the early stage, and there remains a lack of time-series correlation patterns of clinical pathway transfer, recurrence, and treatment. In this study, the artificial immune system (AIS) combined with bootstrap sampling was compared with other machine learning techniques, which included both supervised and unsupervised learning categories. The back propagation neural network, support vector machine (SVM) with a radial basis function kernel, fuzzy c-means, and ant k-means were compared with the proposed method to verify the sensitivity and specificity of the datasets, and the important factors of recurrent endometrial cancer were predicted. In the unsupervised learning algorithms, the AIS algorithm had the highest accuracy (83.35%), sensitivity (77.35%), and specificity (92.31%); in supervised learning algorithms, the SVM algorithm had the highest accuracy (97.51%), sensitivity (95.02%), and specificity (99.29%). The results of our study showed that histology and chemotherapy are important factors affecting the prediction of recurrence. Finally, behavior code and radiotherapy for recurrent endometrial cancer are important factors for future adjuvant treatment.Chang Chih-YenLu Yen-Chiao (Angel)Ting Wen-ChienShen Tsu-Wang (David)Peng Wen-ChenDe Gruyterarticleartificial immune systemsbootstrap samplingbiomimetic intelligencerecurrent endometrial cancersMedicineRENOpen Medicine, Vol 16, Iss 1, Pp 237-245 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	artificial immune systems bootstrap sampling biomimetic intelligence recurrent endometrial cancers Medicine R
spellingShingle	artificial immune systems bootstrap sampling biomimetic intelligence recurrent endometrial cancers Medicine R Chang Chih-Yen Lu Yen-Chiao (Angel) Ting Wen-Chien Shen Tsu-Wang (David) Peng Wen-Chen An artificial immune system with bootstrap sampling for the diagnosis of recurrent endometrial cancers
description	Endometrial cancer is one of the most common gynecological malignancies in developed countries. The prevention of the recurrence of endometrial cancer has always been a clinical challenge. Endometrial cancer is asymptomatic in the early stage, and there remains a lack of time-series correlation patterns of clinical pathway transfer, recurrence, and treatment. In this study, the artificial immune system (AIS) combined with bootstrap sampling was compared with other machine learning techniques, which included both supervised and unsupervised learning categories. The back propagation neural network, support vector machine (SVM) with a radial basis function kernel, fuzzy c-means, and ant k-means were compared with the proposed method to verify the sensitivity and specificity of the datasets, and the important factors of recurrent endometrial cancer were predicted. In the unsupervised learning algorithms, the AIS algorithm had the highest accuracy (83.35%), sensitivity (77.35%), and specificity (92.31%); in supervised learning algorithms, the SVM algorithm had the highest accuracy (97.51%), sensitivity (95.02%), and specificity (99.29%). The results of our study showed that histology and chemotherapy are important factors affecting the prediction of recurrence. Finally, behavior code and radiotherapy for recurrent endometrial cancer are important factors for future adjuvant treatment.
format	article
author	Chang Chih-Yen Lu Yen-Chiao (Angel) Ting Wen-Chien Shen Tsu-Wang (David) Peng Wen-Chen
author_facet	Chang Chih-Yen Lu Yen-Chiao (Angel) Ting Wen-Chien Shen Tsu-Wang (David) Peng Wen-Chen
author_sort	Chang Chih-Yen
title	An artificial immune system with bootstrap sampling for the diagnosis of recurrent endometrial cancers
title_short	An artificial immune system with bootstrap sampling for the diagnosis of recurrent endometrial cancers
title_full	An artificial immune system with bootstrap sampling for the diagnosis of recurrent endometrial cancers
title_fullStr	An artificial immune system with bootstrap sampling for the diagnosis of recurrent endometrial cancers
title_full_unstemmed	An artificial immune system with bootstrap sampling for the diagnosis of recurrent endometrial cancers
title_sort	artificial immune system with bootstrap sampling for the diagnosis of recurrent endometrial cancers
publisher	De Gruyter
publishDate	2021
url	https://doaj.org/article/75de332d8e204106861f52ad59daec38
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An artificial immune system with bootstrap sampling for the diagnosis of recurrent endometrial cancers

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