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Table 3 Performance of the random forest algorithm in predicting LTFU in HIV care, Ethiopia

From: Development of a machine learning prediction model for loss to follow-up in HIV care using routine electronic medical records in a low-resource setting

ML algorithm

Class

Sensitivity (%)

Specificity (%)

Precision (%)

F1 score (%)

MCC (%)

Area under ROC (%)

Area under PRC (%)

RF

Not LTFU

85.7

82.4

84.6

85.2

68.3

89.5

87.5

LTFU

82.4

85.7

83.7

83.1

68.3

89.5

88.7

Weighted average

84.2

84.0

84.2

84.2

68.3

89.5

88.0

  1. Abbreviations: LTFU = loss to follow-up, MCC = Matthews correlation coefficient, ML = machine learning, RF = random forest. ROC = receiver operating characteristic curve, PRC = precision-recall curve
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BMC Medical Informatics and Decision Making

ISSN: 1472-6947

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