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

Author:

Chang Chih-Yen12,Lu Yen-Chiao (Angel)3,Ting Wen-Chien4,Shen Tsu-Wang (David)56,Peng Wen-Chen7

Affiliation:

1. Department of Medical Education and Research, Jen-Ai Hospital , Taichung , Taiwan

2. Department of Elderly Care, Central Taiwan University of Science and Technology , Taichung , Taiwan

3. School of Nursing, College of Medicine, Chung-Shan Medical University , Taichung , Taiwan

4. Division of Colorectal Surgery, Department of Surgery, Chung Shan Medical University Hospital , Taichung , Taiwan

5. Department of Automatic Control Engineering, Feng Chia University , No. 100, Wenhwa Road, Seatwen , Taichung , 40724 , Taiwan

6. Master’s Program in Biomedical Informatics and Biomedical Engineering, Feng Chia University , No. 100, Wenhwa Road, Seatwen , Taichung , 40724 , Taiwan

7. Department of Long-Term Care, Jen-Ai hospital , Taichung , Taiwan

Abstract

Abstract 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.

Publisher

Walter de Gruyter GmbH

Subject

General Medicine

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Prospects and Challenges of Deep Learning in Gynaecological Malignancies;2024

2. Endometriosis Labelling using Machine learning;2023 4th International Conference on Communication, Computing and Industry 6.0 (C216);2023-12-15

3. Evaluating the use of machine learning in endometrial cancer: a systematic review;International Journal of Gynecologic Cancer;2023-09

4. Gynecological cancer prognosis using machine learning techniques: A systematic review of the last three decades (1990–2022);Artificial Intelligence in Medicine;2023-05

5. Bootstrapping methods in computing confidence interval: Real data application;WOMEN IN PHYSICS: 7th IUPAP International Conference on Women in Physics;2023

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