Prediction of Significant Heart Attack Patterns Using Clustering Algorithm

Abstract

This system presents an efficient approach for discovering significant patterns from the heart disease database for heart attack prediction. The heart disease data warehouse is clustered using Kmeans clustering algorithm to extract related data. The primary intent of the system is to design and develop an efficient approach for extracting patterns, which are significant to heart attack, from the heart disease database. The diagnosis of diseases is a significant and tedious task in medicine. The detection of heart disease from various factors or symptoms is a multi-layered issue which is not free from false presumptions often accompanied by unpredictable effects. Thus the effort to utilize knowledge and experience of numerous specialists and clinical screening data of patients collected in databases to facilitate the diagnosis process is considered a valuable option. The proposed system aims to utilize the data mining techniques: clustering and frequent pattern mining.