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

• Published in: [Proceedings] 1992 IEEE International Conference on Systems, Man, and Cybernetics pp. 748 - 753 vol.1
• Main Authors: Goodman, P.H., Kaburlasos, V.G., Egbert, D.D., Carpenter, G.A., Grossberg, S., Reynolds, J.H., Rosen, D.B., Hartz, A.J.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 1992
• Subjects: Fuzzy logic; Fuzzy neural networks; Fuzzy set theory; Hospitals; Linear discriminant analysis; Lungs; Neural networks; Resonance; Simultaneous localization and mapping
• ISBN: 0780307208; 9780780307209
• DOI: 10.1109/ICSMC.1992.271536

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.< >