Table 4.

Classification accuracy of machine learning algorithms compared with BioCAT

Classification	Algorithm	Hessian	Shape	Shape + Size
Sex	Random forest (10)	85.0 %	92.3 % (±3.7)	94.7 % (±2.6)
	Random forest (1,000)	85.0 %	96.1 % (±2.2)	95.9 % (±2.1)
	SVM	81.7 %	99.0 % (±1.2)	99.0 % (±1.2)
Genotype	Random forest (10)	52.0 %	43.3 % (±3.5)	44.7 % (±3.7)
	Random forest (1,000)	46.7 %	69.1 % (±3.4)	70.2 % (±2.8)
	SVM	43.3 %	75.1 % (±2.8)	75.8 % (±2.7)

Hessian column represents accuracy of classifications based on Hessian features extracted with BioCAT. Shape column represents classification accuracy based on landmarks and semi-landmarks, not including centroid. Shape + size represents classification accuracy based on landmarks and semi-landmarks, including centroid