Heart Disease Prediction using Data Mining Techniques

Data mining is a technique that is performed on large databases for extracting hidden patterns by using combinational strategy from statistical analysis, machine learning and database technology. Further, the medical data mining is an extremely important research field due to its importance in the development of various applications in flourishing healthcare domain. While summarizing the deaths occurring worldwide, the heart disease appears to be the leading cause. The identification of the possibility of heart disease in a person is complicated task for medical practitioners because it requires years of experience and intense medical tests to be conducted. In this work, three data mining classification algorithms like Random Forest, Decision Tree and Naïve Bayes are addressed and used to develop a prediction system in order to analyse and predict the possibility of heart disease. The main objective of this significant research work is to identify the best classification algorithm suitable for providing maximum accuracy when classification of normal and abnormal person is carried out. Thus prevention of the loss of lives at an earlier stage is possible. The experimental setup has been made for the evaluation of the performance of algorithms with the help of heart disease benchmark dataset retrieved from UCI machine learning repository. It is found that Random Forest algorithm performs best with 81% precision when compared to other algorithms for heart disease prediction.