Figures 7e and 7f demonstrate that both kernels were effective in classifying the 120 testing samples, achieving diagnostic accuracies of 100%. In general, SVM is known to perform efficiently in high-dimensional spaces; in this case, the dimension of the voltage responses is ℝ and the SVM models were still able to construct the classification hyperplanes with ease. Another key advantage of SVM is its memory efficiency since it only needs the set of support vectors to make a prediction rather than the whole dataset . The large number of parameters in the neural networks makes them relatively memory inefficient in comparison.