Neural networks have been developed for a large number of applications in economy, computer science, telecommunication and medicine. Chapter 1 contains a brief overview of neural networks that have been developed for clinical decision support. Clinical application of neural networks is problematic because of their black-box nature. In the dissertation, different techniques that characterize the properties of a trained neural network are suggested. To enhance the application of neural networks, the topic of missing data is also addressed.
Neural networks can process combinations of qualitative and quantitative data.
The c classes can be decisions such as diagnoses or therapies. The mapping is performed by a neural network, more specifically by the weighted connections between the input, hidden and output layers. During training of a neural network, the weights are adapted to minimize a function that measures the difference between the correct output of the learning cases and the output from the neural network. In chapter 2, metrics are defined that characterize the performance of a trained neural network. The performance is measured by letting the neural network classify a set of test cases of which the true class label is known.