Ignite Creation Date:
2024-05-06 @ 3:36 PM
Last Modification Date:
2024-10-26 @ 1:52 PM
Study NCT ID:
NCT04682756
Status:
UNKNOWN
Last Update Posted:
2020-12-31
First Post:
2020-12-19
Brief Title:
A Multicenter Study on Early Diagnosis of NSTE-ACS Patients Based on Machine Learning Model
Sponsor:
First Affiliated Hospital of Xinjiang Medical University
Organization:
First Affiliated Hospital of Xinjiang Medical University
Study Overview
Official Title:
A Multicenter Study on Early Diagnosis of NSTE-ACS Patients Based on Machine Learning Model
Status:
UNKNOWN
Status Verified Date:
2020-12
Last Known Status:
ACTIVE_NOT_RECRUITING
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief 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
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None