Validity of Machine Learning in Predicting Giant Cell Arteritis Flare After Glucocorticoids Tapering.

ABSTRACT:

Background

Inferential statistical methods failed in identifying reliable biomarkers and risk factors for relapsing giant cell arteritis (GCA) after glucocorticoids (GCs) tapering. A ML approach allows to handle complex non-linear relationships between patient attributes that are hard to model with traditional statistical methods, merging them to output a forecast or a probability for a given outcome.

Objective

The objective of the study was to assess whether ML algorithms can predict GCA relapse after GCs tapering.

Methods

GCA patients who underwent GCs therapy and regular follow-up visits for at least 12 months, were retrospectively analyzed and used for implementing 3 ML algorithms, namely, Logistic Regression (LR), Decision Tree (DT), and Random Forest (RF). The o

SUBMITTER: Venerito V 

PROVIDER: S-EPMC9017227 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature