Table 1.

Performance of machine-learning algorithms and logistic regression in obesity prediction.

Metrics	Gradient boosting, mean (95% CI)	Random forest, mean (95% CI)	Support vector machine, mean (95% CI)	Logistic regression, mean (95% CI)
Accuracya	0.73 (0.72-0.75)	0.73 (0.71-0.74)	0.72 (0.71-0.73)	0.71 (0.70-0.72)
Sensitivitya	0.67 (0.65-0.69)	0.60 (0.58-0.62)	0.65 (0.62-0.67)	0.56 (0.54-0.58)
Specificitya	0.78 (0.76-0.79)	0.82 (0.80-0.83)	0.77 (0.76-0.79)	0.82 (0.81-0.83)
Area under the curveb	0.81 (0.79-0.82)	0.80 (0.79-0.81)	0.80 (0.78-0.81)	0.78 (0.77-0.80)

^aIn these rows, 95% CIs were calculated assuming Gaussian distribution of the proportions.

^bIn this row, 95% CIs were derived through resampling with the bootstrap percentile method with 2000 repetitions.