Heart Disease Prediction System Using SVM and Naive Bayes

As large amount of data is generated in medical organisations (hospitals,medical centers)but as this data is not properly used. There is a wealth of hidden information present in the datasets. This unused data can be converted into useful data. For this purpose we can use different data mining techniques. This paper presents a classifier approach for detection of heart disease and shows how support vector machine(SVM) and Naive Bayes can be used for classification purpose. In our system, we will categories medical data into five categories namely no, low, average,high and very high. Also, if unknown sample comes then the system will predict the class label of that sample. Hence two basic functions namely classification (training) and prediction (testing) will be performed. Accuracy of the system is depends on algorithm and database used.