where is the train split fraction set at 0.8. Thus, the models were trained using 960 random samples from and , and were tested using the remaining 240 samples. From Figure , it is evident that the diagnostic accuracy for all models was improved by incorporating data from both MEAs into the training set. This ensured that the models were fitted to the health states of both MEAs, rather than being constrained to one as observed in Figure . The 1D-CNN and linear-SVM performed similarly well, achieving diagnostic accuracies of 99.2% and 98.8%, respectively. The DNN achieved a slightly lower accuracy of 97.5% while the RBF-SVM performed notably worse, misclassifying 13 starvation samples as normal.