Heart Disease Prediction with Data Mining Clustering Algorithms (original) (raw)

Vast number of people annually suffer from heart malfunction worldwide. Various symptoms result in heart disease which in many cases is hard to diagnose a patient as a heart patient. 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 clustering 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 for 5 stages, and 40 approaches, are compared. Three clusters with an accuracy of 100% are introduced as the highest performance algorithms.