Table 1 Overall model performance for RF, LR, SVM and MLP models in terms of balanced accuracy (BACC), AUPRC, false positive rate (FPR), and false negative rate (FNR) with 95% confidence intervals

From: Population-level predictive variation in machine learning diagnosis of symptomatic bacterial vaginosis

METRIC

RF

LR

SVM

MLP

BACC

0.90 [0.86, 0.94]

0.92 [0.89, 0.95]

0.90 [0.88, 0.92]

0.90 [0.87, 0.92]

AUPRC

0.96 [0.93, 0.99]

0.95 [0.92, 0.98]

0.93 [0.88, 0.97]

0.95 [0.93, 0.98]

FPR

0.10 [0.05, 0.15]

0.07 [0.02, 0.11]

0.08 [0.04, 0.13]

0.07 [0.02, 0.12]

FNR

0.10 [0.05, 0.16]

0.10 [0.06, 0.14]

0.11 [0.07, 0.16]

0.13 [0.10, 0.17]

  1. Bold values indicate the best performing machine learning algorithm for each metric.
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