Table 2A.

Results of supervised learning algorithms on stress recognition in automobile drivers dataset.

Datasets	Classifiers	Feature	Test-train split	Classification accuracy	Precision	Recall	F1-score
Stress recognition in automobile drivers dataset	Logistic regression	Heart rate and respiratory rate		59.3%	0.59	0.59	0.59
	Gaussian Naive Bayes			56.5%	0.60	0.59	0.59
	Decision tree			63.4%	0.64	0.64	0.63
	Random forest			65.0%	0.65	0.66	0.65
	AdaBoost			66.8%	0.67	0.66	0.65
	KNN = 5			63.7%	0.63	0.63	0.63
	KNN = 2			58.1%	0.60	0.57	0.56
Stress recognition in automobile drivers dataset	Logistic regression	Heart rate		58.4%	0.59	0.58	0.58
	Gaussian Naive Bayes			56.0%	0.59	0.56	0.55
	Decision tree			61.9%	0.66	0.062	0.57
	Random forest		70-30 %	56.2%	0.56	0.56	0.56
	AdaBoost			61.5%	0.61	0.61	0.60
	KNN = 5			54.4%	0.54	0.54	0.54
	KNN = 2			51.7%	0.55	0.52	0.50
Stress recognition in automobile drivers dataset	Logistic regression	Respiratory rate		63.2%	0.70	0.63	0.55
	Gaussian Naive Bayes			63.4%	0.72	0.63	0.55
	Decision tree			62.4%	0.64	0.62	0.63
	Random forest			56.9%	0.57	0.57	0.57
	AdaBoost			66.8%	0.66	0.67	0.67
	KNN = 5			59.5%	0.59	0.60	0.59
	KNN = 2			54.0%	0.58	0.54	0.53