Exploration of Drug Science

Table 4. Definition of prediction model evaluation indicators [42].

Name	Definition description
Sensitivity	The proportion of samples that are truly positive and are correctly predicted as positive by the model. $S e n s i t i v i t y = \frac{T P}{T P + F N} \times 100$
Specificity	Among all the samples that are truly negative cases, the proportion of those that were correctly predicted as negative by the model. $S p e c i f i c i t y = \frac{T N}{T N + F P} \times 100$
Accuracy	The proportion of samples that were correctly predicted (whether positive or negative) in all the samples. $A c c u r a c y = \frac{T P + T N}{T P + F P + T N + F N} \times 100$
Precision	The proportion of samples that are truly positive among all those predicted as positive by the model. $P r e c i s i o n = \frac{T P}{T P + F P} \times 100$
F-measure	The harmonic mean of precision and recall. $F-measure = \frac{2 T P}{2 T P + F P + F N} \times 100$
MCC	A balanced metric used to measure the performance of binary classification, with its value ranging from –1 to +1. +1 indicates perfect prediction, 0 indicates random prediction, and –1 indicates completely incorrect prediction. $M C C = \frac{T P \times T N - F P \times F N}{\sqrt{(T P + T N) (T P + F N) (T N + F P) (T N + F N)}} \times 100$

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FN: false negative; FP: false positive; MCC: Matthews correlation coefficient; TN: true negative; TP: true positive.

Declarations

Author contributions

YZ: Conceptualization, Investigation, Writing—original draft, Writing—review & editing. ZW: Investigation, Writing—original draft, Writing—review & editing. HZ: Validation, Writing—review & editing, Supervision, Funding acquisition. All authors read and approved the submitted version.

Conflicts of interest

The authors declare that they have no conflicts of interest.

Ethical approval

Not applicable.

Consent to participate

Not applicable.

Consent to publication

Not applicable.

Availability of data and materials

Not applicable.

Funding

National Natural Science Foundation of China [82073767]; Priority Academic Program Development of Jiangsu Higher Education Institutions [PAPD 2023007]; 2026 College Students’ Innovation and Entrepreneurship Training Program [2026303]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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