Missing data imputation, prediction, and feature selection in diagnosis of vaginal prolapse

Missing data imputation, prediction, and feature selection in diagnosis of vaginal prolapse

Abstract Background Data loss often occurs in the collection of clinical data. Directly discarding the incomplete sample may lead to low accuracy of medical diagnosis. A suitable data imputation method can help researchers make better use of valuable medical data. Methods In this paper, five popular...

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Bibliographic Details
Main Authors: Mingxuan FAN, Xiaoling Peng, Xiaoyu Niu, Tao Cui, Qiaolin He
Format: Article
Language:English
Published: BMC 2023-11-01
Series:BMC Medical Research Methodology
Subjects:
Missing data imputation
Generative adversarial imputation
Feature selection
Classification
Diagnosis of vaginal prolapse
Online Access:https://doi.org/10.1186/s12874-023-02079-0

Internet

https://doi.org/10.1186/s12874-023-02079-0

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