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Table 2 Sensitivity, specificity, post-test probability, and accuracy of six machine learning models for assessing the age of 14

From: Predicting chronological age of 14 or 18 in adolescents: integrating dental assessments with machine learning

 

LR

RF

DT

SVM

KNN

BNB

Sensitivity

0.910

0.893

0.897

0.905

0.910

0.909

Specificity

0.705

0.691

0.687

0.693

0.700

0.703

Post-test probability

0.935

0.931

0.931

0.932

0.934

0.935

Accuracy

0.830

0.826

0.818

0.819

0.814

0.830

  1. LR, Linear regression; RF, random forest; DT, decision tree; SVM, support vector machine; KNN, K-nearest neighbor; BNB, Bernoulli Naive Bayes
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BMC Pediatrics

ISSN: 1471-2431

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