In this age of computer science each and every thing becomes intelligent and perform task as human. For that purpose there are various tools, techniques and methods are proposed. Support vector machine is a model for statistics and computer science, to perform supervised learning, methods that are used to make analysis of data and recognize patterns. SVM is mostly used for classification and regression analysis. And in the same way k-nearest neighbor algorithm is a classification algorithm used to classify data using training examples. In this paper we use SVM and KNN algorithm to classify data and get prediction (find hidden patterns) for target. Here we use medical patients nominal data to classify and discover the data pattern to predict future disease, Uses data mining which is use to classify text analysis in future.

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Cardiovascular disease (CVD) is a big reason of morbidity and mortality in the current living style. Identification of Cardiovascular disease is an important but a complex task that needs to be performed very minutely, efficiently and the correct automation would be very desirable. Every human being can not be equally skillful and so as doctors. All doctors cannot be equally skilled in every sub specialty and at many places we don't have skilled and specialist doctors available easily. An automated system in medical diagnosis would enhance medical care and it can also reduce costs. In this study, we have designed a system that can efficiently discover the rules to predict the risk level of patients based on the given parameter about their health. The rules can be prioritized based on the user's requirement. The performance of the system is evaluated in terms of classification accuracy and the results shows that the system has great potential in predicting the heart disease risk level more accurately.

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