Multiclass Arrhythmia Classification Based on Support Vector Machine Optimized by Grasshopper Optimization Algorithm

An optimal method for diagnosing heart disease using combination of grasshopper evalutionary algorithm and support vector machines

“…The variable b represents the width from the origin for the hypersurface that possesses the maximum separation border. When the variable b is excluded, the resulting solution set consists solely of hypersurfaces that intersect the origin [ 8 , 65 ]. The vertical displacement of the hypersurface from the origin can be determined by dividing the magnitude of the parameter b by the length w .…”

“…There have been attempts since the late 1980s to retrieve the data hidden in this massive volume of data that can't be retrieved with the traditional techniques for accessing and retrieving data from databases [ 6 ]. The intense competition in scientific, social, political, and military fields has made it necessary to design systems that can, on the one hand, quickly find the information users need with little human intervention and, on the other hand, find appropriate analysis methods [ [7] , [8] , [9] ]. Conversely, a large amount of data was utilized to sense it [ 10 , 11 ].…”