Key result
A hybrid genetic neural network system using major clinical risk factors predicted the risk of heart disease with an accuracy of 89%.
Why the study?
Does a genetic neural network based data mining technique accurately predict heart disease using clinical risk factors?
Does a genetic neural network based data mining technique accurately predict heart disease using clinical risk factors?
A hybrid genetic neural network model using common clinical risk factors can predict heart disease risk with 89% accuracy, potentially serving as an early warning system prior to clinical testing.
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Supports further development of neural network models for heart disease risk assessment; hypothesis-generating and requires prospective validation before clinical use.
Amin et al. (2013) studied Heart disease. Genetic neural network based data mining technique vs. Back propagation neural network was evaluated on Prediction accuracy of heart disease risk. A hybrid genetic neural network system using major clinical risk factors predicted the risk of heart disease with an accuracy of 89%.
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