Processing imbalanced medical data at the data level with assisted-reproduction data as an example-Reference-Cited by-同舟云学术

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Processing imbalanced medical data at the data level with assisted-reproduction data as an example

Published:2024-09-04 Issue:1 Volume:17 Page:
ISSN:1756-0381
Container-title:BioData Mining
language:en
Short-container-title:BioData Mining

Author:

Zhu Junliang,Pu Shaowei,He Jiaji,Su Dongchao,Cai Weijie,Xu Xueying,Liu Hongbo

Funder

The Science Research Project of Education Department of Liaoning Province

The Science and Technology Planning Project of Liaoning Province

The Science Research Project of Shenyang City

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1186/s13040-024-00384-y.pdf

Reference44 articles.

1. Beam AL, Kohane IS. Big data and machine learning in health care. JAMA. 2018;319(13):1317–18.

2. Lu S, Yang J, Gu Y, He D, Wu H, Sun W, et al. Advances in machine learning processing of big data from disease diagnosis sensors. ACS Sens. 2024;9(3):1134–48.

3. Shi SN, Li J, Zhu D, Yang F, Xu Y. A hybrid imbalanced classification model based on data density. Inf Sci. 2023;624:50–67.

4. Zhao JK, Jin J, Chen S, Zhang RF, Yu BL, Liu QF. A weighted hybrid ensemble method for classifying imbalanced data. Knowl-Based Syst. 2020;203:106087.

5. Rahman MM, Davis DN. Addressing the class imbalance problem in medical datasets. Int J Mach Learn Comput. 2013;3(2):224.

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