Heart Disease Prediction Using the Data mining Techniques


Heart disease is a major cause of transience in modern society. Due to time and cost constraints, most of the people rely on health care systems to obtain healthcare services. Healthcare organizations collect and produce large volumes of data on daily basis. Information technology allows automatization of processes for extraction of data that help to get interesting knowledge and regularities. In this paper we are using the Data mining techniques like K Means and Weighted Association rule for the elimination of manual tasks and easier extraction of data directly from electronic records, transferring onto secure electronic system of medical records which will save lives and decrease the cost of the healthcare services. K-means clustering is a usually used data clustering for unsupervised learning tasks. Decision tree is used to prediction process. The WAC has been used to get the significant rule instead of flooded with insignificant relation and the Apriori algorithm is used to find out the frequent item set from the patient database KeywordsData Mining, patient records, frequent itemset, decision tree, association rules & clustering.

2 Figures and Tables

Cite this paper

@inproceedings{Wilson2014HeartDP, title={Heart Disease Prediction Using the Data mining Techniques}, author={Aswathy Wilson and Gloria Wilson and Likhiya Joy K}, year={2014} }