Heart Disease Prediction using Data Mining Techniques

Abstract

Studies conducted by World Health Organization and Centres for Disease Control and Prevention have shown that heart diseases have appeared as the primary cause of deaths. Heart disease is responsible for deaths in all age groups and is frequent among all genders. A good answer to the issue of heart diseases is to be able to predict what a patient’s health situation will be in the near future so the doctors can start treatment much sooner which will present good results. Data mining techniques are very efficient to less the diagnostic errors to better the patient’s good health. By utilizing data mining techniques, it takes shorter time for the prediction of the disease with more accuracy. The purpose of this paper is to process a heart disease dataset and draw fine distinctions to predict the disease in the future using data mining techniques. We will be able to decide efficient algorithm from this paper which could be used to predict the disease the application of classification techniques like Random Forest, Naïve Bayes, KNN and Decision tree for the detection of heart disease has been used. Classification tree uses many factors including age, blood sugar and blood pressure; it can discern the probability of patients fallen in CD by using few diagnostic tests which save money and time.