Kennedy, R.L., Harrison, R.F. and Marshall, S.J. (1994) A Comparison of Logistic Regression and Artificial Neural Network Models for the Early Diagnosis of Acute Myocardial Infarction. Research Report. ACSE Research Report 539 . Department of Automatic Control and Systems Engineering
Abstract
An artificial neural network model (ANN) for the early diagnosis of myocardial infarction (AMI) was developed and tested using data from 1470 patients presenting with acute chest pain. The model used 39 clinical and electrocardiographic data items available at the time of presentation. Its performance was well tested on data from two centres-Edinburgh and Sheffield. The performance of the model was compared with that of a logistic regression (LR) model and with the diagnostic performance of the Accident and Emergency Doctors. The LR and ANN models were derived from subsets of the Edinburgh patients......
Metadata
| Item Type: | Monograph |
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| Authors/Creators: |
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| Copyright, Publisher and Additional Information: | The Department of Automatic Control and Systems Engineering research reports offer a forum for the research output of the academic staff and research students of the Department at the University of Sheffield. Papers are reviewed for quality and presentation by a departmental editor. However, the contents and opinions expressed remain the responsibility of the authors. Some papers in the series may have been subsequently published elsewhere and you are advised to cite the later published version in these instances. |
| Dates: |
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| Institution: | The University of Sheffield |
| Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) > ACSE Research Reports |
| Depositing User: | MRS ALISON THERESA BARNETT |
| Date Deposited: | 15 Jul 2014 11:18 |
| Last Modified: | 03 Nov 2016 02:31 |
| Status: | Published |
| Publisher: | Department of Automatic Control and Systems Engineering |
| Series Name: | ACSE Research Report 539 |
| Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:79775 |
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