ARTMAP-IC and medical diagnosis: Instance counting and inconsistent cases

For complex database prediction problems such as medical diagnosis, the ARTMAP-IC neural network adds distributed prediction and category instance counting to the basic fuzzy ARTMAP system. For the ARTMAP match tracking algorithm, which controls search following a predictive error, a new version facilitates prediction with sparse or inconsistent data. Compared to the original match tracking algorithm (MT þ ), the new algorithm (MT ¹ ) better approximates the real-time network differential equations and further compresses memory without loss of performance. Simulations examine predictive accuracy on four medical databases: Pima Indian diabetes, breast cancer, heart disease, and gall bladder removal. ARTMAP-IC results are equal to or better than those of logistic regression, K nearest neighbour (KNN), the ADAP preceptron, multisurface pattern separation, CLASSIT, instance-based (IBL), and C4. ARTMAP dynamics are fast, stable, and scalable. A voting strategy improves prediction by trai...

• Type: Journal
• Year: 1998
• Where: NN
• Authors: Gail A. Carpenter, Natalya Markuzon
• Keywords: ARTMAP-IC Neural Network, Complex Database Prediction, Neural Networks, NN 1998, Predictions