The Healthcare industry is generally “information rich”, but unfortunately not all the data are mined which is required for discovering hidden patterns & effective decision making. Data mining techniques are used to notice knowledge in database and for medical research, mainly in Heart disease prediction. This paper has analyzed prediction system for Heart disease using more number of input attributes. The system uses medical terms such as sex, blood pressure, cholesterol Family history, Smoking , Poor diet , High blood pressure , High blood cholesterol , Obesity , Physical inactivity , Hyper tension etc like 13 attributes to predict the likelihood of patient getting a Heart disease. This research thesis added two more attributes i.e. obesity and smoking. The data mining classification techniques, namely Decision Trees, Naive Bayes, and Support vector machine are analyzed on Heart disease database. The show of these techniques is compared, based on accuracy. As per our results accuracy of Support Vector machine, Decision Trees, and Naive Bayes are 98%,85.5%, and 65.74% respectively. Our analysis shows that out of these three classification models support vector machine predicts Heart disease with highest accuracy.

Data Mining, Naïve Bayes, Support Vector Machine, Decision Tree, Weka Tool

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