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 types with true positive rates of up to 94% in hereditary AE due to C1 inhibitor
deficiency (C1INH), with a Percentage Accuracy of 892% and a Kappa value
of 818% across the six AE types, with a high agreement with the diagnoses
made by experts.Discussion This is the first ever reported ML algorithm designed to
pre-assess to aid AE diagnosis.
