Comparison of Feature Selection Methods on Arrhythmia Dataset

Cardiac arrhythmia is a common sign of heart disease. In modern society, heart disease is always one of the main diseases threatening human health. Medical instruments collect related attributes to make better diagnosis prediction of the disease. This paper applies different feature selection methods including filters and wrappers combining with machine learning methods (SVM, Naive Bayes, Random Forest, C4.5) on the arrhythmia dataset to compare their performances. Results show that filters and wrappers perform both well while filters cost less time. Among them, random forest with the wrapper method has the highest accuracy.