Table 4.

Mean and standard deviation (in square brackets) of model accuracy, balanced accuracy, F1 score, precision, recall, and area under the curve (AUC) produced in supervised one-against-all classification from random forests (RF), support vector machines (SVM), and decision trees (DT).

PPA		RF	SVM	DT
nfvPPA	*Accuracy	79% [36%]	84% [31%]	79% [36%]
	Balanced Accuracy	71% [40%]	71% [40%]	68% [41%]
	F1 score	71% [41%]	70% [41%]	68% [41%]
	Precision	71% [40%]	71% [42%]	71% [42%]
	Recall	71% [40%]	70% [40%]	68% [41%]
	AUC	78% [25%]	71% [36%]	78% [25%]
lvPPA	*Accuracy	64% [42%]	57% [43%]	57% [42%]
	Balanced Accuracy	64% [42%]	57% [43%]	57% [42%]
	F1 score	63% [43%]	55% [44%]	58% [42%]
	Precision	64% [45%]	56% [46%]	63% [46%]
	Recall	64% [42%]	57% [43%]	57% [41%]
	AUC	64% [23%]	57% [17%]	43% [32%]
svPPA	*Accuracy	77% [40%]	69% [43%]	73% [38%]
	Balanced Accuracy	77% [40%]	69% [43%]	73% [38%]
	F1 score	76% [41%]	70% [43%]	72% [39%]
	Precision	76% [42%]	72% [44%]	74% [41%]
	Recall	77% [40%]	69% [43%]	73% [38%]
	AUC	87% [22%]	81% [35%]	75% [25%]

Note. Shown with boldface are the models that provided the highest classification accuracy. The star symbol (*) indicates nonweighted measures; weighted measures consider the unbalance in a design. PPA = primary progressive aphasia; nfvPPA = nonfluent PPA variant; lvPPA = logopenic PPA variant; svPPA = semantic PPA variant.