Table 4.

Performance of the classification models, broken down by each item.

	MDS-UPDRS classifier		Binary classification
	Balanced Accuracy	Acceptable Accuracy	Accuracy	AUCROC
Finger Tapping	0.44	0.84	0.71	0.79
Hand Movements	0.43	0.86	0.74	0.81
Pronation-Supination	0.40	0.81	0.73	0.75
Toe Tapping	0.44	0.88	0.76	0.84
Leg Agility	0.52	0.91	0.80	0.86
All items	0.45	0.86	0.75	0.81

For the MDS-UPDRS rating classifier; balanced accuracy (average class recall) and acceptable accuracy (proportion of predictions within ±1). For the binary classifier; accuracy and area under the receiver operator characteristic curve (AUROC). For each of these evaluation metrics, there was a small variation between items, with pronation-supination tending to perform worse, and leg agility better.