## 4 Conclusions  
SLT plays an important role in building a classifier for prediction and classification in the medical diagnosis of lung cancer under available training samples or other prior  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Number of Classes**} & \multicolumn{2}{c}{**\(n\) = 3000**} & \(n\) = 6000 & \(n\) = 9000 & \(n\) = 12,000 & \(n\) = 15,000 & \(n\) = 18,000 & \(n\) = 21,000 & \(n\) = 24,000 \\ \hline \multirow{8}{*}{95\% confidence (d = 2)} & c = 2 & 0.1103 & 0.0833 & 0.0710 & 0.0635 & 0.0583 & 0.0544 & 0.0514 & 0.0490 \\  & c = 4 & 0.1841 & 0.1386 & 0.1176 & 0.1047 & 0.0957 & 0.0890 & 0.0838 & 0.0795 \\  & c = 6 & 0.2157 & 0.1624 & 0.1377 & 0.1225 & 0.1120 & 0.1040 & 0.0978 & 0.0928 \\  & c = 8 & 0.2416 & 0.1820 & 0.1542 & 0.1372 & 0.1253 & 0.1164 & 0.1094 & 0.1037 \\  & c = 10 & 0.2639 & 0.1989 & 0.1686 & 0.1499 & 0.1369 & 0.1272 & 0.1195 & 0.1132 \\ \hline \multirow{8}{*}{90\% confidence (d = 2)} & c = 2 & 0.1091 & 0.0825 & 0.0704 & 0.0630 & 0.0578 & 0.0540 & 0.0510 & 0.0486 \\  & c = 4 & 0.1835 & 0.1382 & 0.1172 & 0.1044 & 0.0955 & 0.0888 & 0.0836 & 0.0793 \\ \cline{1-1}  & c = 6 & 0.2151 & 0.1621 & 0.1374 & 0.1223 & 0.1117 & 0.1038 & 0.0976 & 0.0926 \\ \cline{1-1}  & c = 8 & 0.2411 & 0.1817 & 0.1540 & 0.1370 & 0.1251 & 0.1163 & 0.1093 & 0.1036 \\ \cline{1-1}  & c = 10 & 0.2634 & 0.1986 & 0.1683 & 0.1497 & 0.1367 & 0.1270 & 0.1193 & 0.1131 \\ \hline \hline \end{tabular} Note: The numbers in orange refer to the achievement of 90% generalization ability.  
\end{table}
Table 2: The relationship between EXP and sample size.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multirow{2}{*}{**Number of Classes**} & \multirow{2}{*}{**Number of Features**} & \multirow{2}{*}{**EXP**} & \multicolumn{2}{c}{**Number of Samples (\(n\) = 0.05)**} \\ \cline{3-4}  & & & & **Actual Value** & **Theoretical Value** \\ \hline [PERSON] et al. [39] & c = 2 & d = 2 & 0.32 & 301 & 235 \\ [PERSON] et al. [40] & c = 2 & d = 10 & 0.31 & 1000 & 776 \\ [PERSON] [41] & c = 2 & d = 40 & 0.47 & 1000 & 958 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The comparison results between the actual and the computed theoretical values on the lower bound of the sample size estimated in Equation (1).  
information. In this paper, the SLT tool was used to analyze two key issues for design classification, i.e., one regarding the computation of the generalization ability of a classifier based on available samples, and its inverse problem, i.e., how many samples are necessary under a certain requirement of prediction accuracy. The research has guiding significance for establishing a necessary sample size under a certain requirement of prediction accuracy, such as the study of the medical diagnosis of lung cancer. Our experimental results validate our method in the process for classifying IA and non-IA. In the future, we will continue exploring the connection between the type of classifiers and generalization ability with SLT.  
Conceptualization, C.M. and S.Y.; methodology, C.M. and S.Y.; software, C.M.; validation, C.M.; writing--original draft preparation, C.M. and S.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under Grant No. 61573251, 61973232.  
The study was conducted in accordance with the Declaration of Helsinki, and approved by the ethics committee of General Hospital of Tianjin Medical University (IRB2020-YX-145-01, 2020/12/31).  
Patient consent was waived due to the secondary use of medical record specimens by the ethics committee of General Hospital of Tianjin Medical University.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflict of interest.