Table 5.

A listing of 27 features that supported the best predicting performance. Among all models, support vector machine-based recursive feature elimination method achieved the best prediction performance using these 27 features.

Gait-Sway = L	Tympany = none	Head Perception = blank
Head Tilt = R	Tympany = blank	Triceps-L = 2
V:A L =2 or 3	Sway Eyes Closed = none	Abdominal = intact
Head Rotation = E/N	Absent Bowel Sounds = present	Biceps-L = blank
Head rotation = L or R	Absent Bowel Sounds = blank	Patellar-L = 2
Gillets = L or R	BR=L=2	BP R/high
Convergence = not E/N	BR-L = blank	Head Perception = ACC measurement
Head Tilt = L	Triceps-L = blank
V:A R = 2	Biceps-L = 2
Sway Eyes Closed = L or R	Abdominal = blank

ACC, accelerometer; L, left; R, right; BR, blink reflex; E/N, abnormality observed; blank, no abnormal observation; V:A R (or L) = 1 (or 2, or 3), width of the vein to width of the artery ratio in the right (or left) eye is 1:1 (or 1:2, or 1:3); triceps (or biceps, or patellar)-L = 2 (normal in a scale of 0–4); BP R/high, blood pressure from the right arm/high readings