Hybrid clustering-classification neural network in the medical diagnostics of reactive arthritis
This work addresses medical diagnostics for reactive arthritis, but it appears incremental as it builds on existing neural network methods with hybrid and fuzzy enhancements.
The authors tackled the problem of overlapping classes in medical diagnostics by proposing a hybrid clustering-classification neural network, which increased processing quality through a fuzzy reasoning procedure and rational learning rate choice, as confirmed by experiments including application to reactive arthritis diagnostics.
The hybrid clustering-classification neural network is proposed. This network allows increasing a quality of information processing under the condition of overlapping classes due to the rational choice of a learning rate parameter and introducing a special procedure of fuzzy reasoning in the clustering process, which occurs both with an external learning signal (supervised) and without the one (unsupervised). As similarity measure neighborhood function or membership one, cosine structures are used, which allow to provide a high flexibility due to self-learning-learning process and to provide some new useful properties. Many realized experiments have confirmed the efficiency of proposed hybrid clustering-classification neural network; also, this network was used for solving diagnostics task of reactive arthritis.