Myocardial Infarction Prediction

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

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.

Inclusion Criteria: patient with chest pain refered to ER with nondiagnostic ECG Exclusion Criteria: 1) Absence of a history of myocardial infarction 2) Absence of bundle branch block, Wolf-Parkinson-White abnormality, ventricular hypertrophy or previous ECG signs of myocardial infarction, 3) Absence of a history of percutaneous coronary surgery or coronary artery bypass grafting, 4) Absence of ECG abnormalities attributable to drugs such as digoxin or tricyclic antidepressants.