. 2023 Sep 14;26(10):107896. doi: 10.1016/j.isci.2023.107896

Table 12.

Evaluation criteria

Name	Formula	Remark
Accuracy	$A c c u r a c y = \frac{T P + T N}{T P + F P + F N + T N}$	A higher accuracy rate represents a larger percentage of the sample that is correctly predicted.
Specificity	$S p e c i f i c i t y = \frac{T N}{T N + F P}$	The higher the specificity, the lower the classification error.
Precision	$P r e c i s i o n = \frac{T P}{T P + F P}$	A higher precision indicates a more accurate prediction of positive cases.
MCC	$M C C = \frac{T P \times T N - F P \times F N}{\sqrt{(T P + F P) \times (T P + F N) \times (T N + F P) \times (T N + F N)}}$	A closer MCC to 1 indicates a more perfect prediction of the subject.
F-measure	$F - m e a s u r e = \frac{T P}{T P + \frac{F N + F P}{2}}$	The F-value represents whether the predicted result is in line with expectations, and the higher the value, the more in line with expectations.