NCT04682756

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

• Status: Unknown
• Phase: —
• Study type: Observational
• Enrollment: 2,500 (actual)
• Sponsor: First Affiliated Hospital of Xinjiang Medical University · Academic / Other
• Sex: All
• Age: 18 Years – 75 Years
• Healthy volunteers: —

Summary

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

Detailed 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.

Conditions

• NSTEMI - Non-ST Segment Elevation MI
• Unstable Angina

Interventions

Type	Name	Description
DIAGNOSTIC_TEST	The model of machine learning	Early diagnosis of NTEMI patients by machine learning model

Timeline

• Start date: 2020-12-20
• Primary completion: 2021-12-20
• Completion: 2022-06-01
• First posted: 2020-12-24
• Last updated: 2020-12-31

Locations

1 site across 1 country: China