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Table 4 Prediction results using receiver operating characteristic (ROC) analysis

From: Predictive modeling in pediatric traumatic brain injury using machine learning

 

Machine learning1,2

Logistic regression3,4

AUC [95% CI]

0.98 [0.95-1]

0.93 [0.87-0.99]

Cutoff score

49

0.25

Sensitivity [95% CI]

94.9% [87.9%-100%]

82.1% [70.0%-94.1%]

Specificity [95% CI]

97.4% [95.0%-99.9%]

92.3% [88.1%-96.5%]

PPV [95% CI]

90.2% [81.2%-99.3%]

72.7% [59.6%-85.9%]

NPV [95% CI]

98.7% [96.9%-100%]

95.4% [92.0%-98.7%]

  1. AUC: area under the curve; CI: confidence interval; PPV: positive predictive value; NPV: negative predictive value.
  2. 1The range of machine learning score is [0, 100].
  3. 2Variables used in the machine learning method were road traffic accident, history of loss of consciousness, vomiting, seizure activity, confusion, clinical signs of skull fracture, and signs of base of skull fracture.
  4. 3The range of logistic regression score is [0, 1].
  5. 4Variables used in the logistic regression model were road traffic accident, history of loss of consciousness, vomiting, and signs of base of skull fracture.
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BMC Medical Research Methodology

ISSN: 1471-2288

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