چكيده به لاتين
Abstract
In the last decade, there has been a growing attention for the study of threatening diseases. Different kinds of diseases such as cancers, heart diseases, MS, Alzheimer, and Epilepsy are hard to cure, since they are not completely curable or their treatment is expensive for a long time.
Therefore, timely diagnosis of the disease along with the correct and timely treatment of the disease lead to either a complete improvement in the disease or increase the patient's life expectancy, while it imposes less costly treatment.
Data mining is one of the most effective tools that helps doctors to better decide on diagnosis, treatment, and other related factors affecting the disease.
This research presents a combination of Particle Swarm Optimization with other Data mining algorithms which is used to diagnose heart disease. This hybrid algorithm provides the highest prediction accuracy compared to non-combinational mode with the help of the minimization of the features.
Key words:
Data mining, Classification, Clustering, Feature selection, Particle Swarm Optimization
