Handling missing data and measurement error for early-onset myopia risk prediction models.

ABSTRACT:

Background

Early identification of children at high risk of developing myopia is essential to prevent myopia progression by introducing timely interventions. However, missing data and measurement error (ME) are common challenges in risk prediction modelling that can introduce bias in myopia prediction.

Methods

We explore four imputation methods to address missing data and ME: single imputation (SI), multiple imputation under missing at random (MI-MAR), multiple imputation with calibration procedure (MI-ME), and multiple imputation under missing not at random (MI-MNAR). We compare four machine-learning models (Decision Tree, Naive Bayes, Random Forest, and Xgboost) and three statistical models (logistic regression, stepwise logistic regression, and least absolute shrinkage an

SUBMITTER: Lai H 

PROVIDER: S-EPMC11378546 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature