Imputation of missing values for cochlear implant candidate audiometric data and potential applications.

ABSTRACT:

Objective

Assess the real-world performance of popular imputation algorithms on cochlear implant (CI) candidate audiometric data.

Methods

7,451 audiograms from patients undergoing CI candidacy evaluation were pooled from 32 institutions with complete case analysis yielding 1,304 audiograms. Imputation model performance was assessed with nested cross-validation on randomly generated sparse datasets with various amounts of missing data, distributions of sparsity, and dataset sizes. A threshold for safe imputation was defined as root mean square error (RMSE) <10dB. Models included univariate imputation, interpolation, multiple imputation by chained equations (MICE), k-nearest neighbors, gradient boosted trees, and neural networks.

Results

Greater quantities of missing da

SUBMITTER: Pavelchek C 

PROVIDER: S-EPMC9901781 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature