ARTMAP-IC AND MEDICAL DIAGNOSIS - INSTANCE COUNTING AND INCONSISTENT CASES

For complex database prediction problems such as medical diagnosis, th e ARTMAP-IC neural network adds distributed prediction and category in stance counting to the basic fuzzy ARTMAP system. For the ARTMAP match tracking algorithm, which controls search following a predictive erro r, a new version facilitates prediction with sparse or inconsistent da ta. Compared to the original match tracking algorithm (MT +), the new algorithm (MT -) better approximates the real-time network differentia l equations and further compresses memory without loss of performance. Simulations examine predictive accuracy on four medical databases: Pi ma Indian diabetes, breast cancer, heart disease, and gall bladder rem oval. ARTMAP-IC results are equal to or better than those of logistic regression, K nearest neighbour (KNN), the ADAP preceptron, multisurfa ce pattern separation, CLASSIT, instance-based (IBL), and C4. ARTMAP d ynamics are fast, stable, and scalable. A voting strategy improves pre diction by training the system several times on different orderings of an input set. Voting, instance counting, and distributed representati ons combine to form confidence estimates for competing predictions. (C ) 1998 Elsevier Science Ltd. All rights reserved.