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 modelling of cohort study datasets. This evaluation is based on real data and predictive models for cardiovascular disease (CVD) risk.

Methods

The data is from a real-world cohort study in Xinjiang, China. It includes personal information, physical examination data, questionnaires, and laboratory biochemical results from 10,164 subjects with a total of 37 variables. Simple imputation (Simple), regression imputation (Regression), expectation-maximization(EM), multiple imputation (MICE) , K nearest

SUBMITTER: Li J 

PROVIDER: S-EPMC10870437 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature