NSTEMI - Non-ST Segment Elevation MI, Unstable Angina
Conditions
Keywords
NSTEMI, UA, Machine Learning
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.
Interventions
DIAGNOSTIC_TESTThe model of machine learning
Early diagnosis of NTEMI patients by machine learning model
Sponsors
Shihezi University
First Affiliated Hospital of Xinjiang Medical University
Study design
Observational model
CASE_CONTROL
Time perspective
OTHER
Eligibility
Sex/Gender
ALL
Age
18 Years to 75 Years
Inclusion criteria
* Patients were included and excluded strictly according to the diagnostic criteria of Chinese guidelines for diagnosis and treatment of Non-STsegment elevation acute coronary syndrome (2016). The patients were admitted to the hospital with chest pain as the main complaint, and were admitted to the first affiliated Hospital of Xinjiang Medical University and the first affiliated Hospital of Medical College of Shihezi Univ- ersity. the patients were diagnosed as NSTEMI and UA by coronary angiography (age range from 30 to 75 years old).
Exclusion criteria
\- 1. Patients with STEMI, aortic dissecting aneurysm, pneumothorax and other non-cardiogenic chest pain. 2.Severe hepatorenal failure, primary tumor without surgical treatment, non-severe infection complicated with shock and pregnant women. 3.Previous severe valvular disease, viral myocarditis, pericardial effusion, cardiac pacemaker implantation, cardiogenic shock with serious complications, hypertensive heart disease, various cardiomyopathy, congenital heart disease, etc. 4.Patients with heart disease, AECOPD, lung tumor and hyperthyroidism were diagnosed in the past.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Accurate diagnosis of NSTEMI from patients with acute chest pain | Within 1 year | NSTEMI patients are accurately diagnosed from patients with acute chest pain through a trained machine learning algorithm. Our model uses multi-fold cross-validation and ROC-AUC curve as the measurement index, 75% of the data are modeled, and 25% of the data verify the effect of the model. For this reason, we will calculate the accuracy, specificity and likelihood ratio when the sensitivity cutoff value is 0.9. |
Countries
China
Outcome results
None listed