Table 3:

Prediction accuracy of each cohort and the balanced accuracy (bAcc) of the four cohorts for different models. Each model is characterized by either multi-class or multi-label prediction and by the datasets used for training (in brackets). The best result in each row is bold.

	Multi-class (All)	Multi-Label
				Ours
		One-Domain (UCSF)	Single-Predictor (ALL)	Two-Domain (UCSF+SRI)	Three-Domain (All)
Control	55.7±8.0%	51.8±10.2%	43.5±13.4% †	51.9±9.1%	51.8±7.5%
CI-only	65.3±6.1% †	73.4±4.8%	71.6±5.3%	76.1±6.7%	74.0±3.4%
HIV-only	24.6±12.3% †*	29.6±4.7% †*	43.6±16.4%	32.9±13.7% *	43.9±13.6%
HAND	49.6±8.6%	42.8±9.3% †	49.6±5.8%	49.6±12.6%	51.0±13.6%
bAcc	48.8±3.6% †	49.4±1.7% †	52.1%±3.7	52.6%±7.6	55.2±4.7%
Std	17.5%	18.4%	13.4%	18.0%	13.2%

^*Accuracy not significantly higher than chance (two-tailed p > 0.05, permutation test)

^†Accuracy significantly lower than the three-domain model (two-tailed p < 0.05, Hardin-Shumway test).