Comparison of the effects of imputation methods for missing data in predictive modelling of cohort study datasets

Comparison of the effects of imputation methods for missing data in predictive modelling of cohort study datasets

Abstract Background Missing data is frequently an inevitable issue in cohort studies and it can adversely affect the study's findings. We assess the effectiveness of eight frequently utilized statistical and machine learning (ML) imputation methods for dealing with missing data in predictive mo...

Full description

Bibliographic Details
Main Authors: JiaHang Li, ShuXia Guo, RuLin Ma, Jia He, XiangHui Zhang, DongSheng Rui, YuSong Ding, Yu Li, LeYao Jian, Jing Cheng, Heng Guo
Format: Article
Language:English
Published: BMC 2024-02-01
Series:BMC Medical Research Methodology
Subjects:
Missing data
Imputation methods
Cohort study
Cardiovascular disease
Machine learning
Online Access:https://doi.org/10.1186/s12874-024-02173-x

Internet

https://doi.org/10.1186/s12874-024-02173-x

Similar Items