Evolving classifiers to inform clinical assessment of Parkinson´s disease

We describe the use of a genetic programming system to induce classifiers that can discriminate between Parkinson´s disease patients and healthy age-matched controls. The best evolved classifer achieved an AUC of 0.92, which is comparable with clinical diagnosis rates. Compared to previous studies of this nature, we used a relatively large sample of 49 PD patients and 41 controls, allowing us to better capture the wide diversity seen within the Parkinson´s population. Classifiers were induced from recordings of these subjects´ movements as they carried out repetitive finger tapping, a standard clinical assessment for Parkinson´s disease. For ease of interpretability, we used a relatively simple window-based classifier architecture which captures patterns that occur over a single tap cycle. Analysis of window matches suggested the importance of peak closing deceleration as a basis for classification. This was supported by a follow-up analysis of the data set, showing that closing deceleration is more discriminative than features typically used in clinical assessment of finger tapping.