Congenital Heart Disease is one of the major causes of deaths in children. However, a proper diagnosis at an early stage can result in significant life saving. Unfortunately, all the physicians are not equally skilled, which can cause for time delay, inaccuracy of the diagnosis. A system for automated medical diagnosis would enhance the accuracy of the diagnosis and reduce the cost effects. In the present paper, a Decision Support System has been proposed for diagnosis of Congenital Heart Disease. The proposed system is designed and developed by using MATLAB's GUI feature with the implementation of Backpropagation Neural Network. The Backpropagation Neural Network used in this study is a multi layered Feed Forward Neural Network, which is trained by a supervised Delta Learning Rule. The dataset used in this study are the signs, symptoms and the results of physical evaluation of a patient. The proposed system achieved an accuracy of 90%.

The fuzzy relational databases which use the special types of constraints, called dependencies, as a semantic tool for expressing properties of the data like a classical relational databases. Fuzzy functional dependencies(ffd) and fuzzy multivalued dependencies(fmvd) are the most common types of dependencies. Similar to classical relational database, a full utilization of fuzzy multivalued dependencies in the design of fuzzy relational databases requires that fuzzy embedded multivalued dependencies(fEmvd) and fuzzy subset dependencies(fsd). These multivalued dependencies hold in a projection of a relation but not necessarily in the relation itself. The main object of this paper is to extend the concept of Embedded Multivalued Dependency (EMVD) and Subset Dependency (SD) to fuzzy relational database.

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