A dual network expert system

The authors describe a methodology for expert systems that combines the learning and inferencing capabilities of artificial neural networks with a user-friendly interactive interface. The network architecture is a dual, three-layered feedforward network of continuous value units, fully interconnected between layers. The network is trained from raw data, avoiding the need for specific knowledge acquisition from domain experts. The system will generate probability estimates for each hypothesis (output, diagnoses) as well as probabilistic pointers toward missed symptoms and supports continuous user interaction. To illustrate the method, the authors consider a medical application, the diagnosis of heart disease from a combination of angiographic and general patient data.<
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