Neural networks: a systematic introduction
Neural networks: a systematic introduction
Neural networks for assessing the failure load of a construction
Journal of Computational and Applied Mathematics - Special issue: Selected papers from the 2nd international conference on advanced computational methods in engineering (ACOMEN2002) Liege University, Belgium, 27-31 May 2002
Using decision tree for diagnosing heart disease patients
AusDM '11 Proceedings of the Ninth Australasian Data Mining Conference - Volume 121
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A brief overview of the principles of Artificial Neural Networks (ANN's) is presented, followed by a review of the state of the art for ANN's in the application field of the diagnosis of cardiovascular diseases. Next the technique of ANN's is applied to model the risk stratification according to D'Agostino et al. [R.B. D'Agostino, M.W. Russell, D.M. Huse, et al., Primary and subsequent coronary risk appraisal: New results from the Framingham study, American Heart Journal 139 (2000) 272-281]. The performance of the network proves its ability to find non-linear relationships in (medical) data and some important factors in accomplishing an accurate and reliable network are derived. At the end an ANN is designed to investigate the predictive quality of certain well chosen risk factors for secondary prevention. The performance of the resulting network is put in an appropriate perspective and some aspects that need further study are mentioned.