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Table 2 Comparison of the predictive performance for six models (testing set)

From: The prediction of asymptomatic carotid atherosclerosis with electronic health records: a comparative study of six machine learning models

Model

Acc (%)

Sp (%)

Pp (%)

Re (%)

F1 (%)

AUCROC

LR

74.7

86.6

68.6

53.2

59.9

0.809

RF

74.5

89.5

71.3

47.2

56.8

0.794

DT

65.4

71.8

51.2

53.8

52.5

0.628

XGB

73.4

87.8

68.0

47.2

55.7

0.788

GNB

67.0

88.0

63.1

37.2

46.8

0.753

KNN

68.8

81.5

57.7

45.6

50.9

0.704

  1. Acc, accuracy; Sp, specificity; Pp, precision; Re, recall; F1, F1 score; AUCROC, the area under the receiver operating characteristic curve; LR, logistic regression; RF, random forest; DT, decision tree; XGB, eXtreme Gradient Boosting; GNB, Gaussian Naïve Bayes; KNN, K-Nearest Neighbour
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BMC Medical Informatics and Decision Making

ISSN: 1472-6947

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