Medical online neuro-fuzzy diagnostics system with active learning

Abstract

Situations when in the medical data set some patients have known diagnoses and all other have unknown ones is spread wise problem of present-day medicine. Known systems of computational intelligence show mediocre level of diagnostics in these data sets. In this paper online neuro-fuzzy diagnostics system with active learning is proposed. This system allows to increase a quality of medical diagnostics under the condition of small number of known reference signals due to combination of special learning algorithms – active learning. The proposed online neuro-fuzzy system is based on popular neural networks as Self-Organizing Map (SOM) and Learning Vector Quantization network (LVQ). Active learning procedure permits to tune their synaptic weights using simple recurrent self-learning procedures (SOM) and controlled learning with teacher (LVQ). Neuro-fuzzy diagnostics system with active learningwas used for breast cancer in Wisconsin data set processing and showed higher level of classification-clusterization results comparatively with known systems