Table 5.

Performance of Machine Learning Classifiers

Dataset	Normalization	Oversampling	AUC	Accuracy	Balanced accuracy	d′	Kappa	Precision	Sensitivity	Specificity
Apple Watch	FALSE	None	0.918 (0.040)	0.918 (0.040)	0.753 (0.083)	2.228 (0.319)	0.544 (0.117)	0.936 (0.050)	0.974 (0.029)	0.532 (0.187)
	FALSE	Oversampling	0.917 (0.037)	0.872 (0.077)	0.825 (0.060)	2.154 (0.377)	0.514 (0.145)	0.962 (0.038)	0.890 (0.095)	0.760 (0.156)
	TRUE	None	0.926 (0.040)	0.928 (0.029)	0.789 (0.063)	2.347 (0.373)	0.602 (0.102)	0.943 (0.039)	0.976 (0.022)	0.602 (0.136)
	TRUE	Oversampling	0.922 (0.041)	0.882 (0.039)	0.853 (0.048)	2.237 (0.398)	0.533 (0.154)	0.967 (0.033)	0.898 (0.049)	0.807 (0.105)
Oura Ring	FALSE	None	0.897 (0.053)	0.914 (0.045)	0.692 (0.083)	1.926 (0.428)	0.441 (0.149)	0.929 (0.050)	0.977 (0.021)	0.407 (0.176)
	FALSE	Oversampling	0.888 (0.063)	0.839 (0.075)	0.780 (0.079)	1.855 (0.451)	0.404 (0.135)	0.960 (0.039)	0.853 (0.096)	0.707 (0.208)
	TRUE	None	0.892 (0.063)	0.906 (0.050)	0.689 (0.082)	1.827 (0.454)	0.418 (0.135)	0.925 (0.051)	0.971 (0.031)	0.407 (0.174)
	TRUE	Oversampling	0.896 (0.062)	0.844 (0.057)	0.805 (0.067)	1.876 (0.462)	0.422 (0.153)	0.964 (0.030)	0.855 (0.071)	0.755 (0.143)
ECG + Link	FALSE	None	0.924 (0.039)	0.917 (0.049)	0.755 (0.070)	2.321 (0.330)	0.560 (0.112)	0.938 (0.047)	0.970 (0.049)	0.540 (0.170)
	FALSE	Oversampling	0.923 (0.037)	0.870 (0.082)	0.830 (0.055)	2.191 (0.350)	0.521 (0.140)	0.966 (0.035)	0.883 (0.105)	0.776 (0.144)
	TRUE	None	0.926 (0.041)	0.922 (0.043)	0.788 (0.065)	2.383 (0.436)	0.595 (0.113)	0.943 (0.040)	0.968 (0.045)	0.607 (0.147)
	TRUE	Oversampling	0.924 (0.042)	0.878 (0.048)	0.856 (0.043)	2.253 (0.372)	0.536 (0.156)	0.970 (0.029)	0.890 (0.059)	0.821 (0.096)

Values in each cell are the mean and SD across nights.