A multi-layer perceptron neural network trained with a genetic algorithm predicted myocardial infarction with 96% accuracy using ECG results, outperforming a radial basis function network (83%).
Observational (n=935)
Do artificial neural networks accurately predict acute myocardial infarction within 2 weeks in patients with chest pain and nondiagnostic ECG?
An MLP neural network trained with a genetic algorithm can predict acute myocardial infarction with 96% accuracy using ECG findings in patients presenting with chest pain and nondiagnostic ECGs.
Absolute Event Rate: 96% vs 83%
Background: 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 to classify the two groups. Results: The RBF neural network had an accuracy of 83 % with ECG findings and an accuracy of 78 % with clinical features. When and clinical data were used in an MLP neural network trained with a genetic algorithm, ECG results led to a classification accuracy of 96 % and clinical data yielded an accuracy of 84.5%. Conclusion: Both neural network structures predicted MI within about 2 weeks of hospital referral with an acceptable degree of accuracy in patients with nondiagnostic ECG. The MLP neural network significantly outperformed the RBF network because of the use of the genetic algorithm, which provided a global strategy to accurately determine MLP weights (clinical trials registry: NCT01870258).
Kojuri et al. (Mon,) conducted a observational in Chest pain with nondiagnostic ECG (n=935). Multi-layer perceptron (MLP) neural network vs. Radial basis function (RBF) neural network was evaluated on Classification accuracy for predicting myocardial infarction using ECG results. A multi-layer perceptron neural network trained with a genetic algorithm predicted myocardial infarction with 96% accuracy using ECG results, outperforming a radial basis function network (83%).
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