Predicting Heart-Diseases from Medical Dataset Through Frequent Itemsets Using Improved Algorithm - Vijayalakshmi, V (International Journal of Computer Sciences and Engineering, 2018)
In health concern business, data mining plays a significant task for predicting diseases. 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. Apriori algorithm is a classical algorithm of association rule mining and widely used for generating frequent item sets. This classical algorithm is inefficient due to so many scans of database. When the database is large, it will take too much time to scan the database and may produce a larger number of candidate item sets. To overcome these limitations, researchers have made a lot of improvements to the Apriori. In this paper, the authors proposed a method to predict the heart disease through frequent itemsets. 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. The aim of our proposed technique is to obtain the frequent symptoms and evaluate the performance of new technique and compare with the existing classical Apriori with support count.