Heart disease prediction using lazy associative classification

What is it about?

Medical data mining is used to extract knowledgeable information from a huge amount of medical data. Associative classification is a rule based new approach which integrates association rule mining and classification, if applied on medical data sets, lends them to an easier interpretation. It selects a small set of high quality rules and uses these rules for prediction. Heart disease rates among the major cause of mortality in developing countries and is rapidly becoming so in developing countries like India. India is the second most populous country in the world with an estimated population of over 1 billion. Rapid industrialization and urbanization have resulted in tremendous growth in the economy over the last deacde.Concurrently India has also seen an exponential rise in prevalence of Heart disease. It has predicted that CVD will be the most important cause of mortality in India by the year 2015, and A.P is in risk of CVD.Hence a decision support system should be proposed to predict the risk score of a patient, which will help in taking precautionary steps like balanced diet and medication which will in turn increase life time of a patient. Through this paper we propose a lazy associative classification for prediction of heart disease in Andhra Pradesh and present some experimental results which will help physicians to take accurate decisions.

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Why is it important?

In this research paper we presented a lazy data mining approach for heart disease classification. We applied information centric arrtibute measure PCA to generate class association rules. This class association rules will be used to predict the occurrence of heart disease. The system is designed for Andhra Pradesh population. Andhra Pradesh is in risk of more death due to heart disease. Heart disease can be handled successfully if more research is encouraged to develop prediction system in this area.