Features selection for arrhythmia diagnosis using Relief-F algorithm and support vector machine

Computer Science

• Feature Extraction 100%
• Support Vector Machine 100%
• Feature Selection 100%
• Experimental Result 33%
• Logistic Regression 33%
• Classification Accuracy 33%
• Classification Models 33%
• Classification Performance 33%
• Extracted Feature 33%
• Electrical Activity 33%
• Effective Method 33%
• Information Gain 33%

Pharmacology, Toxicology and Pharmaceutical Science

• Heart Arrhythmia 100%

Chemical Engineering

• Support Vector Machine 100%