Table 2 Performance metrics of the machine learning algorithms

TE75

0.64 (0.42–0.82)

0.63 (0.40–0.83)

0.62 (0.45–0.79)

0.80 (0.57–1.00)

0.43 (0.15–0.71)

0.67 (0.30–1.00)

0.60 (0.37–0.80)

0.69 (0.48–0.83)

TE300

0.57 (0.33–0.78)

0.52 (0.28–0.70)

0.42 (0.23–0.62)

0.30 (0.08–0.58)

0.54 (0.27–0.80)

0.44 (0.18–0.69)

0.40 (0.10–0.73)

0.33 (0.10–0.56)

b0

0.66 (0.46–0.84)

0.73 (0.48–0.89)

0.55 (0.39–0.71)

0.81 (0.60–1.00)

0.26 (0.07–0.50)

0.60 (0.17–1.00)

0.54 (0.35–0.74)

0.65 (0.44–0.81)

b10

0.53 (0.33–0.74)

0.58 (0.37–0.79)

0.49 (0.31–0.66)

0.68 (0.47–0.89)

0.24 (0.06–0.47)

0.40 (0.09–0.71)

0.52 (0.32–0.71)

0.59 (0.40–0.75)

b200

0.79 (0.61–0.93)

0.72 (0.51–0.91)

0.68 (0.52–0.84)

0.56 (0.31–0.81)

0.81 (0.57–1.00)

0.63 (0.41–0.84)

0.75 (0.50–1.00)

0.64 (0.40–0.82)

b800

0.64 (0.44–0.82)

0.74 (0.55–0.88)

0.56 (0.42–0.72)

0.64 (0.40–0.83)

0.47 (0.23–0.71)

0.53 (0.27–0.79)

0.57 (0.37–0.77)

0.59 (0.40–0.76)

ADC

0.72 (0.50–0.90)

0.75 (0.54–0.94)

0.68 (0.53–0.82)

0.80 (0.60–0.95)

0.54 (0.27–0.78)

0.69 (0.36–0.91)

0.68 (0.50–0.87)

0.74 (0.56–0.87)