Fuzzy ARTMAP neural network compared to linear discriminant analysis prediction of the length of hospital stay in patients with pneumonia

Author(s): Carpenter, G.A. | Egbert, D. | Goodman, P. | Grossberg, S. | Hartz, A.J. | Kaburlasos, V. | Reynolds, J.H. | Rozen, D.B. |

Year: 1992

Citation: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics. (Chicago, October, 1992) I, New York: IEEE Press, 748 753.

Abstract: On a database derived from patients hospitalized with pneumonia, the authors compared the cross-validated predictions of linear discriminant analysis (LDA) to a new self-organizing supervised neural network that incorporates fuzzy set logic into adaptive resonance theory mapping (ARTMAP) to simultaneously predict outcome and define category patterns with outcomes. The purpose of this study was to determine whether such a self-organizing neural network could accurately predict the length of stay of patients admitted to a community hospital with a diagnosis of pneumonia. Unbiased proportionate reduction in error using ARTMAP was 50% greater than LDA. Under conditions of simulated noise and increasing-proportion learning, ARTMAP demonstrated further advantages over LDA

Topics: Machine Learning, Applications: Medical Diagnosis, Models: Fuzzy ARTMAP,

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