Early Heart Disease Prediction using Frequent Pattern Mining Techniques

The successful application of data mining in highly visible fields like e-business, marketing and retail has led to its application in other industries and sectors. Among these sectors just discovering is healthcare. The Healthcare industry is generally “information rich”, but unfortunately not all the data are mined which is required for discovering hidden frequent patterns & effective decision making. Discovery of hidden patterns and relationships often goes unexploited. Advanced data mining modeling techniques can help remedy this situation.Data mining is a process which finds useful patterns from large amount of data. Data items are frequent in itemset is to be organized in multilevel and multi dimensional way. Data mining is the process of discovering interesting knowledge such as Patterns and Associations. The process of looking for patterns to document is called pattern mining. Pattern mining is a data mining method that involves finding existing patterns in the data. Mining frequent patterns is probably one of the most important concepts in data mining. Graph transformation method is used for mining of patterns in frequent itemset. An itemset is closed if none of its immediate supersets has the same support as the itemset. Frequent itemsets are so important . This paper intends to use data mining Classification Modeling Techniques, namely, Decision Trees, Naïve Bayes and Neural Network, along with weighted association Apriori algorithm in Heart Disease Prediction.