Prediction of Heart Disease by Mining Frequent Items and Classification Techniques

These days immense abstraction of data yields and collected in every instance of time. So to analyze them is the toughest task to do. This immense volume of data has been generated from unlike sources like health care, social media, business applications, manufacturing industries and many more. HealthCare plays a pivotal role in Big Data. Spotting and safeguarding of the diseases at a primitive stage are very much crucial. Heart disease specifically implies the condition of the heart that contracts or obstructs blood vessels which result in pain in chest and heart attack. This paper emphasizes on the diagnosis of heart diseases at a primitive stage so that it will lead to a successful cure of the diseases. In this paper, frequent item mining is used for filtering the attributes and diverse data mining classification method like Decision tree classification, Naive Bayes classification, Support Vector Machine classification, and k-NN classification are used for determination and safeguard of the diseases at an early stage so that it can be treated and preventable.