Data mining techniques are used in the field of medicine for various purposes. Mining association rule is one of the interesting topics in data mining which is used to generate frequent itemsets. It was first proposed for market basket analysis. Researchers proposed variations in techniques to generate frequent itemsets. Generating large number of frequent itemsets is a time consuming process. In this paper, the authors devised a method to predict the risk level of the patients having heart disease through frequent itemsets. The dataset of various heart disease patients are used for this research work. Frequent itemsets are generated based on the chosen symptoms and minimum support value. The extracted frequent itemsets help the medical practitioner to make diagnostic decisions and determine the risk level of patients at an early stage. The proposed method can be applied to any medical dataset to predict the risk factors with risk level of the patients based on chosen factors. An experimental result shows that the developed method identifies the risk level of patients efficiently from frequent itemsets.
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