Key result
Unsupervised neural networks achieve ~91% accuracy in identifying CHF, outperforming supervised models.
Why the study?
A new technique using spectral analysis and neural networks for identification of patients with congestive heart failure from normal controls was investigated.
Does an unsupervised neural network improve classification accuracy of congestive heart failure compared to a supervised neural network using R-R interval spectral analysis?
Does an unsupervised neural network improve classification accuracy of congestive heart failure compared to a supervised neural network using R-R interval spectral analysis?
Absolute Event Rate: 91.43% vs 83.65%
Unsupervised neural networks using R-R interval spectral analysis achieved 91.43% accuracy in identifying patients with congestive heart failure, outperforming supervised neural networks.
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Hypothesis-generating for unsupervised neural networks in CHF detection; prospective validation required before clinical adoption.
Elfadil et al. (2009) studied Congestive heart failure (n=94). Unsupervised neural networks (Kohonen self organizing maps) vs. Supervised neural networks (back-propagation) was evaluated on Classification accuracy. Unsupervised neural networks achieved a classification accuracy of 91.43% for identifying congestive heart failure, compared to 83.65% with supervised neural networks.
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