The local connectivity and convolution of the 1D-CNN promote its superiority over the other models since they significantly reduced the dimensionality of the AC voltage response input, allowing the model to fit more closely to the training data and achieve higher diagnostic accuracies. Although it was incapable of predicting the SoH of an MEA that was not included in the training set, it responded well to the inclusion of small amounts of new training data, attaining an accuracy of 95.1% when only 20% of the new data was added to the training set.