  This research is to search for alternatives to the resolution of complex
medical diagnosis where human knowledge should be apprehended in a general
fashion. Successful application examples show that human diagnostic
capabilities are significantly worse than the neural diagnostic system. This
paper describes a modified feedforward neural network constructive algorithm
(MFNNCA), a new algorithm for medical diagnosis. The new constructive algorithm
with backpropagation; offer an approach for the incremental construction of
near-minimal neural network architectures for pattern classification. The
algorithm starts with minimal number of hidden units in the single hidden
layer; additional units are added to the hidden layer one at a time to improve
the accuracy of the network and to get an optimal size of a neural network. The
MFNNCA was tested on several benchmarking classification problems including the
cancer, heart disease and diabetes. Experimental results show that the MFNNCA
can produce optimal neural network architecture with good generalization
ability.
