Classification of angioedema types using decision tree modeling.

ABSTRACT:

Introduction

All angioedema (AE) presents with transient, localized swelling; however, the underlying causes, prognosis, and treatments vary significantly. Consequently, identifying a specific AE type is challenging.

Methods

We aimed to apply a machine learning (ML) model to improve AE diagnosis. Random forest (RF) ML was used to create a prediction model for diagnosing correct AE types. Development comprised a literature search to establish AE's clinical characteristics, developing and translating questions in collaboration with 12 European AE centers, and selecting, testing, validating and optimizing the established ML model. Analysis included 342 specialist-diagnosed patients with one of six AE types.

Results

The final optimized RF model correctly identified AE typ

SUBMITTER: Aulenbacher F 

PROVIDER: S-EPMC12833243 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature