Improving Classifications of Medical Data Based on Fuzzy ART2 Decision Trees
Analyzing given medical databases provide valuable references for classifying other patients symptoms. This study presents a strategy for discovering fuzzy decision trees from medical databases, in particular Harbeman's Survival database and the Blood Transfusion Service Center database. Harbeman's Survival database helps doctors treat and diagnose a group of patients who show similar past medical symptoms and the Blood Transfusion Service Center database advises individuals about when to donate blood. The proposed data mining procedure involves neural network based clustering using Adaptive Resonance Theory 2 (ART2), and the extraction of fuzzy decision trees for each homogeneous cluster of data records using fuzzy set theory. Besides, another objective of this paper is to examine the effect of the number of membership functions on building decision trees. Experiments confirm that the number of erroneously clustered patterns is significantly reduced compared to other methods without preprocessing data using ART2.
medical data fuzzy decision tree data classification fuzzy art2 algorithm