Table 4 Machine learning algorithm performance for the top 5 models identified by traditional programming versus MILO.

A. Traditional programming

Logistic regression

86 (80–90)

0.96 (0.88–1.00)

98 (89–100)

82 (75–88)

16^a

Deep neural network

81 (75–86)

0.96 (0.85–1.00)

94 (83–99)

77 (70–83)

10^b

k-nearest neighbor

81 (75–86)

0.92 (0.84–1.00)

98 (89–100)

76 (68–82)

10^b

Support vector machine

85 (79–89)

0.97 (0.86–1.00)

98 (89–100)

81 (74–87)

14^c

Random forest

79 (73–85)

0.92 (0.84–1.00)

94 (83–99)

75 (67–82)

10^b

B. MILO

k-nearest neighbor

90 (85–94)

0.96 (0.85–1.00)

96 (86–99)

88 (82–93)

5^e

Logistic regression

87 (81–91)

0.95 (0.83–1.00)

98 (89–100)

83 (77–89)

23^f

Naïve bayes

89 (84–93)

0.95 (0.84–1.00)

94 (83–99)

87 (81–92)

11^d

Random forest

84 (79–89)

0.94 (0.84–1.00)

96 (86–99)

81 (74–87)

23^f

Deep neural network

84 (79–89)

0.95 (0.85–1.00)

100 (93–100)

80 (72–86)

17 ^g

Support vector machine

86 (80–90)

0.97 (0.87–1.00)

98 (89–100)

82 (75–88)

11^d

Gradient boosting machine

81 (75–86)

0.94 (0.88–1.00)

96 (86–99)

76 (69–83)

5^e