Machine-Learning Models With Multiple Imputation With Sequential Nearest Neighbors Imputation for Predicting the Prognosis of Idiopathic Sudden Sensorineural Hearing Loss Patients.

ABSTRACT:

Objectives

The prognosis of idiopathic sudden sensorineural hearing loss (ISSNHL) is heterogeneous. The study aimed to investigate the prognostic factors of ISSNHL and develop machine-learning models with multiple imputation to predict the prognosis of ISSNHL.

Design

A retrospective study was undertaken on a cohort of 600 patients with ISSNHL who underwent standardized treatment protocols. Clinical features, blood tests, concurrent symptoms, body measures, and audiometric features were collected. Missing values were imputed by multiple imputation with the sequential nearest neighbors algorithm. Six classifiers and four classification tasks were explored. Model performance was evaluated by accuracy and area under the receiver operating characteristic curve. Furthermore, a fea

SUBMITTER: Jin Y 

PROVIDER: S-EPMC12700688 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature