Table 9.

Algorithms and related accuracy.

	Scenario I		Scenario II		Scenario III		Scenario IV		Scenario V
	Input Variables:		Input Variables:		Input Variables:		Input Variables:		Input Variables:
	Tskin, EDA, HR, To and RH		Tskin, EDA, HR and To		Tskin, EDA, HR and RH		Tskin, EDA and HR		Tskin, EDA, To and RH
Algorithms	Avg.	St. dev.	Avg.	St. dev.	Avg.	St. dev.	Avg.	St. dev.	Avg.	St. dev.
Logistic Regression	0.81409	0.01097	0.66468	0.020608	0.658721	0.013551	0.50145	0.01582	0.821118	0.015817
Linear Discriminant Analysis	0.834002	0.014409	0.679365	0.014593	0.712757	0.014929	0.508934	0.016283	0.837188	0.016283
K-Nearest Neighbors	0.939725	0.009485	0.807953	0.016847	0.874745	0.014654	0.628515	0.003083	0.991965	0.003083
Classification and Regression Trees	0.991964	0.003655	0.96564	0.006938	0.966609	0.006322	0.809057	0.002703	0.993211	0.00266
Gaussian Naive Bayes	0.829985	0.012559	0.707909	0.02119	0.789527	0.011923	0.537479	0.011854	0.809613	0.011854
Support Vector Machines	0.953167	0.009965	0.803516	0.025457	0.879319	0.019446	0.62186	0.005874	0.980602	0.005874