Data Mining Classification Algorithms for Heart Disease Prediction

Abstract

Abstract-Annually 17.3 million people approximately die from heart disease worldwide. A heart patient shows various symptoms and it is hard to attribute them to the heart disease in different steps of disease progress. Data mining, as a solution to extract hidden pattern from the clinical dataset are applied to a database in this research. The database consists of 209 instances and 8 attributes. All available algorithms in classification technique, are compared to achieve the highest accuracy. To further increase the accuracy of the solution, the dataset is preprocessed by different supervised and unsupervised algorithms. The system was implemented in WEKA and prediction accuracy in 9 stages, and 396 approaches, are compared. Random tree with an accuracy of 97.6077% and lowest errors is introduced as the highest performance algorithm.