An early aortic dissection screening model and applied research based on ensemble learning.

ABSTRACT:

Background

As a particularly dangerous and rare cardiovascular disease, aortic dissection (AD) is characterized by complex and diverse symptoms and signs. In the early stage, the rate of misdiagnosis and missed diagnosis is relatively high. This study aimed to use machine learning technology to establish a fast and accurate screening model that requires only patients' routine examination data as input to obtain predictive results.

Methods

A retrospective analysis of the examination data and diagnosis results of 53,213 patients with cardiovascular disease was conducted. Among these samples, 802 samples had AD. Forty-two features were extracted from the patients' routine examination data to establish a prediction model. There were five ensemble learning models applied to explo

SUBMITTER: Liu L 

PROVIDER: S-EPMC7791246 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature