TABLE 2.

Dependence of learner performance on algorithm parameters

Analysis of Model Variables		Stationary Model Variables	Accuracy		Bland-Altman Analysis
			RMSE (mm Hg)	R2	Mean (mm Hg)	Upper LOA (mm Hg) Lower LOA (mm Hg)
Train/test split ratio	80%/20%	Random forest, CBF, MAP, & HR	0.81	0.98	0.01	1.6	−1.57
	70%/30%		0.86	0.98	0.03	1.71	−1.66
	60%/40%		0.98	0.97	−0.02	1.9	−1.93
	50%/50%		1.06	0.97	−0.0	2.07	−2.08
Regressor type	Random forest	80%/20% split, CBF, MAP, & HR	0.81	0.98	0.01	1.6	−1.57
	Bagging		0.94	0.97	0.01	1.85	−1.84
Feature performance	CBF, MAP, & HR	Random forest & 80%/20% split	0.81	0.98	0.01	1.6	−1.57
	CBF & MAP		1.03	0.97	−0.03	1.99	−2.05
	CBF & HR		1.77	0.91	0.02	3.49	−3.44
	CBF features only		2.38	0.82	−0.03	4.64	−4.69
	MAP & HR only		2.79	0.76	0.04	5.51	−5.43

LOA = limit of agreement.