Predicting risk of an adverse event in complex medical data sets using fuzzy ARTMAP network

Author(s): Ash, A.S. | Carpenter, G.A. | Gaehde, S.A. | Markuzon, N. | Moskowitz, M.A. |

Year: 1994

Citation: Artificial Intelligence in Medicine: Interpreting Clinical Data. Technical Report Series, Menlo Park, CA: AAAI Press, 93-96.

Abstract: Fuzzy ARTMAP is a supervised learning system which includes nonlinear dynamics in the learning process. We introduce a new testing procedure which allows the system to estimate the probability of an outcome. Simulations illustrate the system performance in estimating risk in medical procedure. The results are compared to the performance of the logistic regression model. It is shown that both models have similar, significant explanatory power.

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

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