One of the commonly occurring diseases across the world is heart disease. About 60 percent of the total population gets affected by the heart disease. Among the several kinds of heart disease, coronary heart disease is dealt in this paper. The healthcare trade gathers enormous amounts of healthcare files which, regrettably, are not ?mined? to determine hidden information for efficient assessment creation. Since enormous sum of people get exaggerated by heart disease, the patients’ case history raise to a maximum extent in hospitals, as the result analyzing becomes a difficult process for medical practitioners. In this paper, an effective method to extract the data from the large amount of documents is proposed using text mining. Using text mining techniques, the required data are extracted in the structured format. This paper uses an apriori algorithm in association rule mining, which is used for frequent item set extraction and rule generation. As the result, several rules will be generated from which the disease can be predicted.
Coronary Heart Disease, Text Mining, Association rule mining, Apriori
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