Description

Data Mining is one of the most motivating area of research that is become increasingly popular in health organization. Data Mining plays an important role for uncovering new trends in healthcare organization which in turn helpful for all the parties associated with this field. The mining of the input data is the most important task in all type of the database management. Associative rule mining is commonly used for mining the input data. The relations between the attributes were estimated and based on that the input data can be represented in binary format. The rule based mining can be most useful in medical research in order to efficiently analyze the symptoms and treatments for the diseases. In the proposed approach a classifier based approach is employed for the classification of the input data based on the Ordered Rule (OR) tree. For generation of rules associative rule mining is employed. The process is done in medical dataset. The Chest pain dataset is selected for this process. The chest pain is normally identified based on the attributes like age, sex, serum cholesterol level, Blood pressure. The main objective of the process is to classify the data based on OR tree. To generate rules for the classification problem based on associate rule mining. To prune the rules based on each attributes of the input data. To check a redundant rule for input database and identify the class for the data in order diagnose chest pain. To employ Ordered Rule based approach for the identification of the arrangement of the attributes.