NCT01870258

Myocardial Infarction Prediction

Prediction of Acute Myocardial Infarction With Artificial Neural Networks in Patients With Nondiagnostic Electrocardiogram

• Status: Completed
• Phase: N/A
• Study type: Interventional
• Enrollment: 1,100 (actual)
• Sponsor: Shiraz University of Medical Sciences · Academic / Other
• Sex: All
• Age: 40 Years – 72 Years
• Healthy volunteers: Not accepted

Summary

prediction of MI in patients with chest pain and nondiagnostic ECG was done in 2 weeks

Detailed description

Myocardial infarction remains one the leading causes of mortality and morbidity and involves a high cost of care. Early prediction can be helpful in preventing the development of myocardial infarction with appropriate diagnosis and treatment. Artificial neural networks have opened new horizons in learning about the natural history of diseases and predicting cardiac disease. Methods: A total of 935 cardiac patients with chest pain and nondiagnostic electrocardiogram (ECG) were enrolled and followed for 2 weeks in two groups based on the appearance of myocardial infarction. Two types of data were used for all patients: nominal (clinical data) and quantitative (ECG findings). Two different artificial neural networks - radial basis function (RBF) and multi-layer perceptron (MLP) - were used.

Conditions

• Myocardial Infarction
• Artificial Neural Network

Interventions

Type	Name	Description
OTHER	ANN prediction of myocardial infarction

Timeline

• Start date: 2011-01-01
• Primary completion: 2012-04-01
• Completion: 2012-06-01
• First posted: 2013-06-06
• Last updated: 2013-06-06