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| Duarte,P.S.; Mastrocolla,L.E.; Farsky,P.S.; Sampaio,C.R.E.P.S.; Tonelli,P.A.; Barros,L.C.; Ortega,N.R.; Pereira,J.C.R.. |
| Coronary artery disease (CAD) is a worldwide leading cause of death. The standard method for evaluating critical partial occlusions is coronary arteriography, a catheterization technique which is invasive, time consuming, and costly. There are noninvasive approaches for the early detection of CAD. The basis for the noninvasive diagnosis of CAD has been laid in a sequential analysis of the risk factors, and the results of the treadmill test and myocardial perfusion scintigraphy (MPS). Many investigators have demonstrated that the diagnostic applications of MPS are appropriate for patients who have an intermediate likelihood of disease. Although this information is useful, it is only partially utilized in clinical practice due to the difficulty to properly... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Fuzzy model; Coronary disease; Scintigraphy; Myocardial perfusion. |
| Ano: 2006 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-879X2006000100002 |
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| Pereira,J.C.R.; Tonelli,P.A.; Barros,L.C.; Ortega,N.R.S.. |
| The present study compares the performance of stochastic and fuzzy models for the analysis of the relationship between clinical signs and diagnosis. Data obtained for 153 children concerning diagnosis (pneumonia, other non-pneumonia diseases, absence of disease) and seven clinical signs were divided into two samples, one for analysis and other for validation. The former was used to derive relations by multi-discriminant analysis (MDA) and by fuzzy max-min compositions (fuzzy), and the latter was used to assess the predictions drawn from each type of relation. MDA and fuzzy were closely similar in terms of prediction, with correct allocation of 75.7 to 78.3% of patients in the validation sample, and displaying only a single instance of disagreement: a... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Epidemiologic methods; Stochastic models; Fuzzy models; Clinical signs; Diagnosis; Data analysis. |
| Ano: 2004 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-879X2004000500012 |
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