MEDICAL DATA ANALYSIS USING SELF-ORGANIZING DATA MINING TECHNOLOGIES

摘要

Three self-organizing data mining technologies that employ complementary descriptive languages ― parametric regression models (GMDH neural networks), fuzzy rules (self-organizing fuzzy rule induction), and similarity models (analog complexing based clustering and classification) ― are applied to generate diagnosis models of different levels of heart disease. The classification results show an accuracy of over 95% in average. Due to the strong knowledge extraction capabilities of the used technologies a nucleus of 4 most relevant variables is identified. The obtained results both classification accuracy and identified nucleus are also important for diagnosis cost reduction considerations.