Predicting olfactory loss in chronic rhinosinusitis using machine learning.

ABSTRACT:

Objective

Compare machine learning (ML)-based predictive analytics methods to traditional logistic regression in classification of olfactory dysfunction in chronic rhinosinusitis (CRS-OD) and identify predictors within a large multi-institutional cohort of refractory CRS patients.

Methods

Adult CRS patients enrolled in a prospective, multi-institutional, observational cohort study were assessed for baseline CRS-OD using a smell identification test (SIT) or brief SIT (bSIT). Four different ML methods were compared to traditional logistic regression for classification of CRS normosmics versus CRS-OD.

Results

Data were collected for 611 study participants who met inclusion criteria between 2011 April and 2015 July. Thirty-four percent of enrolled patients demonstrated ol

SUBMITTER: Ramakrishnan VR 

PROVIDER: S-EPMC8558487 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature