The Use of Data Mining Techniques in Heart Disease Prediction

One-third of deaths worldwide are the result of heart disease, the rate of death from heart disease is higher than the mortality rates due to cancer. The cause of these deaths is the lack of knowledge of the symptoms of this disease or lack of attention to these symptoms. Where the patient believes that these symptoms due to fatigue or other diseases less serious. And as a result of the enormous amounts of data in the field of heart disease and the corresponding development in the field of computing and the availability of data processing programs. It becomes easy now to use these programs to predict heart disease. In this article we used Weka software as one of Data Mining techniques in heart disease prediction by testing heart-c.arff dataset obtained from UCI repository against several data classification techniques using naïve bayes and J84 classification algorithm.