Automated diagnosis of coronary artery disease based on data mining and fuzzy modeling.

A fuzzy rule-based decision support system (DSS) is presented for the diagnosis of coronary artery disease (CAD). The system is automatically generated from an initial annotated dataset, using a four stage methodology: 1) induction of a decision tree from the data; 2) extraction of a set of rules from the decision tree, in disjunctive normal form and formulation of a crisp model; 3) transformation of the crisp set of rules into a fuzzy model; and 4) optimization of the parameters of the fuzzy model. The dataset used for the DSS generation and evaluation consists of 199 subjects, each one characterized by 19 features, including demographic and history data, as well as laboratory examinations.

Distinct association of SLC19A1 polymorphism -43T>C with red cell folate levels and of MTHFR polymorphism 677C>T with plasma folate levels.

Objectives: The role of SLC19A1 -43T>C, MTHFR 677C>T and MS 2756A>G polymorphisms on red cell and plasma folate levels.

Design And Methods: Genotype analysis of the three polymorphisms. Red cell and plasma folate measurements in 64 patients with coronary artery disease.