Table 10.

The correct classification rates (P_CCR), sensitivity (P_sens), and specificity (P_spec) percentages for the classification procedures based on models M_I (V^APC, D) − M_III (V^APC, D) and M_I (V, V^APC, D) − M_III (V, V^APC, D) using metric distance, and APC in hippocampal volumes with threshold probabilities with optimum threshold values π_o = π_opt based on the cost function C₁(π_o, w₁, w₂) with w₁ = w₂ = 1 and w₁ = 1, w₂ = 3 (top); and with optimum threshold values π_o = π_opt based on the cost function C₂(π_o, η₁, η₂) with η₁ = η₂ = 0.5 and η₁ = .3, η₂= 0.7 (bottom).

Using optimum πo based on cost function C1 (πo, w1, w2 ) with
	w1 = w2 = 1			w1 = 1, w2 = 3
	MI (V APC, D)	MII (V APC, D)	MIII (V APC, D)*	MI (V APC, D)*	MII (V APC, D)	MIII (V APC, D)
πopt	.61	.53-.73	.35-.40	.56	.35-.36	.31-.32
PCCR	84%	82%	80%	80%	70%	75%
Psens	72%	67%	78%	78%	78%	83%
Pspec	92%	92%	81%	81%	65%	69%
Using optimum πo based on cost function C2(πo, η1, η2) with
	η1 = η2 = 0.5			η1 = .3, η2 = 0.7
	MI (V APC, D)*	MII (V APC, D)	MIII (V APC, D)	MI (V APC, D)*	MII (V APC, D)	MIII (V APC, D)
πopt	.61	.53-.73	.49	.56	.23-.25	.31-.32
PCCR	84%	82%	82%	80%	55%	75%
Psens	72%	67%	67%	78%	100%	83%
Pspec	92%	92%	92%	81%	23%	69%
Using optimum πo based on cost function C1 (πo, w1, w2) with
	w1 = w2 = 1			w1 = 1, w2 = 3
	MI (V, V APC, D)	MII (V, V APC, D)	MIII (V, V APC, D)*	MI (V, V APC,D)	MII (V, V APC,D)	MIII (V, VAPC,D)*
πopt	.56-.57	.48-.51	.39-.42	.56-.57	.48-.51	.39-.42
PCCR	89%	89%	91%	89%	89%	91%
Psens	94%	89%	94%	94%	89%	94%
Pspec	85%	88%	88%	85%	88%	88%
Using optimum πo based on cost function C2(πo, η1, η2) with
	η1 = η2 = 0.5			η1 = .3, η2 = 0.7
	MI (V, V APC,D)	MII (V, V APC,D)	MIII (V, V APC,D)*	MI (V, V APC,D)	MII (V, V APC,D)	MIII (V, V APC,D)*
πopt	.56-.57	.48-.51	.39-.42	.56-.57	.48-.51	.39-.42
PCCR	89%	89%	91%	89%	89%	91%
Psens	94%	89%	94%	94%	89%	94%
Pspec	85%	88%	88%	85%	88%	88%

^*The models with the best classification performance are marked with an asterisk.