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.
