However, as mentioned previously, perfect diagnostic accuracy suggests that there are clear margins of separation between each SoH class; SVM is known to perform poorly when the training data has more noise; hence, when additional data is passed to the SVM model from a different MEA or operating condition, its accuracy could be significantly reduced if there is any overlap between the datasets. This issue was investigated further by Sabzekar et al., who used fuzzy SVMs to differentiate between outliers and reduce the effect of noisy data, however, this was not explored here .