Use of neural networks to diagnose acute myocardial infarction. II. A clinical application

Abstract

We investigated the ability of neural networks to diagnose acute myocardial infarction (AMI) from laboratory data only. Several networks were trained with different combinations of data obtained at admission and within the first 12 h and 24 h after admission. The data used included the electrocardiogram (ECG) and the concentrations in serum of potassium, creatine kinase B-subunit (CKB), and lactate dehydrogenase isoenzyme 1 for 250 patients with suspected AMI. Based on admission data, the correct diagnosis was predicted for 76% of the patients in the test group from the ECG data only, and the best combination of ECG results with other variables yielded correct diagnoses for 85% of the test group. Using all of the data available within 24 h, the network predicted the correct diagnosis for 99% of the test data. Almost the same high predictability was obtained by using only two CKB values-recorded at admission and within 12 h after admission-or by using just the latter one. Neural networks and quadratic discriminant analysis performed similarly, but the neural networks were more robust for combinations with many laboratory data.