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A Multicenter Study on Early Diagnosis of NSTE-ACS Patients Based on Machine Learning Model

Unstable Angina Clinical Trial

Official title:
A Multicenter Study on Early Diagnosis of NSTE-ACS Patients Based on Machine Learning Model

Clinical Trial Summary

Early diagnosis of NSTEMI and UA patients is mainly through the construction of machine learning model.

Clinical Trial Description

The patients with NSTEMI and UA were included. After manual labeling, the admiss- ion record characteristics of patients were selected. 75% of the data is used to build the model, and 25% of the data is used to verify the validity of the model. Five classification models of one-dimensional convolution (CNN), naive Bayesian (NB), support vector machine (SVM), random forest (RF) and ensemble learning were constructed to identify and diagnose NSTEMI and UA patients. Multi-fold cross-validation and ROC-AUC curve are used to measure the advantages and disadvantages of the models. ;

Study Design

Related Conditions & MeSH terms

Clinical Trial Details
NCT number NCT04682756
Study type Observational [Patient Registry]
Source First Affiliated Hospital of Xinjiang Medical University
Contact
Status Active, not recruiting
Phase
Start date December 20, 2020
Completion date June 1, 2022
View Details
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