Why did they need machine learning? It seems from Figures 2B-G that there's a clear cut off. "Moreover, to create a classifier for ME/CFS patients capable of identifying new patients, required for a robust diagnostic tool, we developed a trained kernel Support Vector Machine (SVM), a supervised machine-learning algorithm, using our experimental data. To classify new patients based on whether they fall to the right of…
Nothing wrong with an SVM. How else would they create a decision boundary for classifying patients? The choice of the polynomial kernel is interesting, but I don't think it causes any issues given the data.
I guess my question comes from the observation that these advanced statistical techniques such as machine learning haven't been around for long and yet medicine has often created decision boundaries, presumably just looking at the data and making a reasonable cutoff. Is all the extra effort in a case like this worth the time investment?