A Decision Tree for Automatic Diagnosis of Parkinson’s Disease from Offline Drawing Samples: Experiments and Findings

We address the problem of designing a machine learning tool for the automatic diagnosis of Parkinson's disease that is capable of providing an explanation of its behavior in terms that are easy to understand by clinicians. For this purpose, we consider as machine learning tool the decision tree, because it provides the decision criteria in terms of both the features which are actually useful for the purpose among the available ones and how their values are used to reach the final decision, thus favouring its acceptance by clinicians. On the other side, we consider the random forest and the support vector machine, which are among the top performing machine learning tool that have been proposed in the literature, but whose decision criteria are hidden into their internal structures. We have evaluated the effectiveness of different approaches on a public dataset, and the results show that the system based on the decision tree achieves comparable or better results that state-of-the-art solutions, being the only one able to provide a plain description of the decision criteria it adopts in terms of the observed features and their values.