Knowledge Discovery Using Associative Classification for Heart Disease Prediction

M. A. Jabbar, B. L. Deekshatulu, Priti Chandra
  • January 2013, Springer Science + Business Media
  • DOI: 10.1007/978-3-642-32063-7_4

What is it about?

Associate classification is a scientific study that is being used by knowledge discovery and decision support system which integrates association rule discovery methods and classification to a model for prediction. An important advantage of these classification systems is that, using association rule mining they are able to examine several features at a time. Associative classifiers are especially fit to applications where the model may assist the domain experts in their decisions. Cardiovascular deceases are the number one cause of death globally. An estimated 17.3 million people died from CVD in 2008, representing 30% of all global deaths. India is at risk of more deaths due to CHD. Cardiovascular disease is becoming an increasingly important cause of death in Andhra Pradesh. Hence a decision support system is proposed for predicting heart disease of a patient. In this paper we propose a new Associate classification algorithm for predicting heart disease for Andhra Pradesh population. Experiments show that the accuracy of the resulting rule set is better when compared to existing systems. This approach is expected to help physicians to make accurate decisions.

Why is it important?

In this paper, we have applied associative classification algorithm to medical health data to explore risk factors associated with heart disease. Associate classifiers are especially fit to applications where the model may assist the domain experts in their decisions. There are many domains such as medical, where the maximum accuracy of the model is desired. The model is applied for Andhra Pradesh population. Andhra Pradesh is at risk of more deaths due to CHD. Coronary heart disease can be handled successfully if more research is encouraged in this area. In the near feature we will investigate extraction of patterns associated with Heart Disease using associative classification and evolutionary algorithms.

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http://dx.doi.org/10.1007/978-3-642-32063-7_4

The following have contributed to this page: Dr AKHIL JABBAR MEERJA

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