# Impact of Dataset Size on Classification Performance:
## 5 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Size Reduction Scenarios**} \\ \cline{3-8}  & & \multicolumn{2}{c}{**LD-SD980**} & \multicolumn{2}{c}{**SD980-SD490**} & \multicolumn{2}{c}{**SD490-SD98**} \\ \cline{3-8}  & & **Diabetes** & \multicolumn{2}{c}{**Skin Segmentation**} & \multicolumn{2}{c}{**Diabetes**} & \multicolumn{2}{c}{**Skin Segmentation**} & \multicolumn{2}{c}{**Skin Segmentation**} \\ \hline \multirow{4}{*}{**Performance Metric**} & Accuracy & **0.01426** & 0.31207 & 0.07215 & 0.18943 & 0.05821 & 0.46414 \\ \cline{2-8}  & Precision & 0.07215 & 0.46812 & 0.10565 & **0.04648** & **0.04746** & 0.5 \\ \cline{2-8}  & Recall & **0.01831** & 0.31207 & 0.2327 & 0.14917 & **0.04746** & 0.46414 \\ \cline{2-8}  & Specificity & **0.01831** & **0.02275** & 0.33724 & 0.05592 & 0.07215 & 0.44828 \\ \cline{2-8}  & F score & **0.01072** & 0.48405 & 0.33724 & **0.02872** & **0.04746** & 0.3707 \\ \cline{2-8}  & AUC & **0.01831** & 0.0951 & 0.26435 & **0.01539** & **0.04746** & 0.4721 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: \(p\)-values for different size reduction scenarios using the SVM model; bold values are significant. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Size Reduction Scenarios**} \\ \cline{3-8}  & & \multicolumn{2}{c}{**LD-SD980**} & \multicolumn{2}{c}{**SD980-SD490**} & \multicolumn{2}{c}{**SD490-SD98**} \\ \cline{3-8}  & & \multicolumn{2}{c}{**D diabetes**} & \multicolumn{2}{c}{**Skin Segmentation**} & \multicolumn{2}{c}{**Diabetes**} & \multicolumn{2}{c}{**Skin Segmentation**} & \multicolumn{2}{c}{**Diabetes**} & \multicolumn{2}{c}{**Skin Segmentation**} \\ \hline \multirow{5}{*}{**Performance Metric**} & Accuracy & **0.00107** & **0.04182** & **0.015** & 0.30153 & 0.07215 & 0.05821 \\ \cline{2-8}  & Precision & **0.00122** & **0.00014** & **0.0044** & 0.42858 & **0.04947** & **0.01618** \\ \cline{2-8}  & Recall & **0.00169** & **0.00169** & **0.015** & 0.37828 & 0.07215 & 0.05821 \\ \cline{2-8}  & Specificity & **0.00122** & **0.00014** & **0.0057** & 0.45224 & **0.04947** & **0.03144** \\ \cline{2-8}  & F score & **0.00122** & **0.00014** & **0.0044** & 0.42858 & **0.04947** & **0.01618** \\ \cline{2-8}  & AUC & **0.00064** & **0.00009** & **0.01659** & 0.42465 & 0.05592 & **0.00494** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: \(p\)-values for different size reduction scenarios using the DT model; bold values are significant. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Size Reduction Scenarios**} \\ \cline{3-8}  & & \multicolumn{2}{c}{**LD-SD980**} & \multicolumn{2}{c}{**SD980-SD490**} & \multicolumn{2}{c}{**SD490-SD98**} \\ \cline{3-8}  & & \multicolumn{2}{c}{**D diabetes**} & \multicolumn{2}{c}{**Skin Segmentation**} & \multicolumn{2}{c}{**Diabetes**} & \multicolumn{2}{c}{**Skin Segmentation**} & \multicolumn{2}{c}{**Diabetes**} & \multicolumn{2}{c}{**Skin Segmentation**} \\ \hline \multirow{5}{*}{**Performance Metric**} & Accuracy & **0.00047** & **0.04648** & 0.18673 & 0.46017 & 0.12302 & 0.05821 \\ \cline{2-8}  & Precision & **0.00031** & **0.00069** & **0.04746** & 0.26109 & 0.20897 & 0.07215 \\ \cline{2-8}  & Recall & **0.00047** & **0.00219** & 0.18673 & 0.46017 & 0.12302 & 0.05821 \\ \cline{2-8}  & Specificity & **0.00031** & **0.00069** & 0.07636 & 0.5 & 0.12714 & **0.03362** \\ \cline{2-8}  & F score & **0.00031** & **0.00069** & **0.04746** & 0.31561 & 0.15151 & 0.0505 \\ \cline{2-8}  & AUC & **0.00009** & **0.00069** & 0.07078 & 0.07215 & **0.02222** & 0.10565 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: \(p\)-values for different size reduction scenarios using the RF model; bold values are significant.