Graph Based Approach for Heart Disease Prediction

What is it about?

The diagnosis of Disease is a significant and tedious task in Medicine. The detection of heart disease from various factors or symptoms is a multilayered issue which is not free from false presumptions often accompanied by unpredictable effects. Thus the attempt to exploit knowledge and experience of several specialists and clinical screening of data of patients collected in data bases to facilitate the diagnosis process is considered a good option. The health care industry collects huge amounts of health care data which unfortunately are not mined, to discover hidden information for effective decision making. Discovery of hidden patterns and relationships often goes unexploited. Advanced data mining techniques can help remedy this situation. Weighted association rule mining is the most useful data mining technique. Weighted association rules are association rules with weights or strength of presence. As data mining techniques are being introduced and widely applied to nontraditional item sets, existing approaches for finding frequent item sets were out of data as they cannot satisfy the requirement of these domains. Hence, an alternative method of modeling the objects in the said data set is graph. Graph based algorithms efficiently solve the problem of mining association rules .In this paper we propose an efficient algorithm which integrates weighted association rule mining and graph based approach for heart disease prediction for Andhra Pradesh population.

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Why is it important?

The objective of our work is to predict more accurately the presence of heart disease. Rules with risk factors like Age, high BP, and cholesterol were extracted. The Results and Discussion section discussed several important association rules predicting presence of heart disease. Graph based weighted association rule mining efficiently solve the problem of mining association rules .In future work we plan to develop data mining system for predicting heart disease using artificial intelligence.