Automated diagnosis and disease characterization using neural network analysis

A neural network approach is used to analyze and diagnose a rather new and uncommon disease, Lyme borreliosis. To fully exploit the method´s generalizing power, a significance analysis split the set of inputs of a trained network into two classes that were important and unimportant. The results of this analysis lead to a new structured network, whose topology and architecture reflect the estimated relevance of symptoms. The diagnostic performance thus obtained showed a dramatic improvement which reached an average error rate of around 6%