Intelligent and Effective Heart Disease Prediction System using Weighted Associative Classifiers
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ABSTRACT:
The healthcare environment is still ‘information rich’ But ‘knowledge poor’. There is a wealth of data available within the health care systems. However, there is a lack of effective analysis tools to discover hidden relationships in data. The aim of this work is to design a GUI based Interface to enter the patient record and predict whether the patient is having Heart disease or not using Weighted Association rule based Classifier. The prediction is performed from mining the patient’s historical data or data
repository. In Weighted Associative Classifier (WAC), different weights are assigned to different attributes according to their predicting capability. It has already been proved that the Associative Classifiers are performing well than traditional classifiers approaches such as decision tree and rule induction. Further from experimental results it has been found that WAC is providing improved
accuracy as compare to other already existing Associative Classifiers. Hence the system is using WAC as a Data mining technique to generate rule base. The system has been implemented in java Platform and trained using benchmark data from UCI machine learning repository. The system is expandable for the new dataset.
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