# Improved War Strategy Optimization with Extreme Learning Machine for Health Data Classification
## Appendix A Results of Classification Precision, Sensitivity, Specificity, F1 Score Values  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Methods** & **Brest** & **Bupa** & **Dermatology** & **Diabetes** & **Hepatitis** & **Lymphography** & **Parkinsons** & **SAheart** & **SPECTF** & **Vertebral** & **WDBC** \\ \hline ELM & 78.70 [11] & * 100.00 [10] & * 79.69 & 84.00 [13] & 65.24 [15] & 91.05 [10] & * 77.85 & 79.12 [10] & N/A & 97.42  
Classification of the Breast dataset by ELM and metaheuristic algorithms is shown in Table 10. It was found that PSO was 100 in the precision values, IWSO was 96.63 in the sensitivity values, PSO was 100 in the specificity values, and IWSO was 98.18 in the F1 score values. In the studies with this dataset, when the accuracy results of IWSO were compared, FPA and GWO [8], as well as RBFs and RBFN-ELM-GA [9] were higher than algorithms, i.e., more successful, than DE [10], it is seen that it is low. In these studies, when the sensitivity results of IWSO were compared, it was seen that FPA and GWO [8] were lower and less successful than RBFs and RBFN-ELM-GA [9] algorithms. When the specificity results of IWSO were compared, it was revealed that it made a higher classification than FPA, GWO, and MNHO [8], RBFs, and RBFN-ELM-GA [9] algorithms. The classification of the Bupa dataset by ELM and metaheuristic algorithms is shown in Table 11. It was found that ALO was 86.60 in the precision values, IWSO was 81.73 in the sensitivity values, GWO was 84.70 in the specificity values, and GWO was 82.17 in the F1 score values. When the accuracy results of IWSO were compared in the studies conducted with these data, ABC [12] was higher than the algorithm, that is, more successful, than HAELM [10] and from ELM [13]. It was determined to be low. It is seen that the precision result of IWSO is higher than HAELM [11], the sensitivity result is higher than ELM, and the specificity result is lower than HAELM [11]. The F1 score of the IWSO is higher than ICSELM [13] and lower than HAELM [11]. Classification of the Dermatology dataset by ELM and metaheuristic algorithms is shown in Table 12. It was found that IWSO was 100 in the precision values, GWO, OWSO, and IWSO were 100 in sensitivity values, IWSO was 100 in specificity values, and IWSO was 100 in F1 score values. When the accuracy results of the IWSO were compared in the studies conducted with these data, it is higher than PCA-KELM [15], i.e., more successful in classification, and lower than FA and ELM [14]. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Bupa**} & \multicolumn{2}{c}{**ALO**} & \multicolumn{2}{c}{**DA**} & \multicolumn{2}{c}{**PSO**} & \multicolumn{2}{c}{**GWO**} & \multicolumn{2}{c}{**WSO**} & \multicolumn{2}{c}{**OWSO**} & \multicolumn{2}{c}{**IWSO**} \\ \cline{2-13}  & **Mean** & **Std.** & **Mean** & **Std.** & **Mean** & **Std.** & **Mean** & **Std.** & **Mean** & **Std.** & **Mean** & **Std.** \\ \hline Precision & 86.60 & 9.82 & 78.54 & 14.64 & 81.72 & 13.63 & 85.38 & 11.45 & 83.37 & 10.99 & 82.60 & 13.61 & 81.13 & 11.04 \\ \hline Sensitivity & 77.76 & 4.68 & 81.44 & 5.80 & 79.25 & 4.34 & 80.18 & 4.63 & 78.44 & 4.21 & 78.16 & 5.00 & 81.73 & 5.41 \\ \hline Specificity & 82.72 & 6.73 & 80.63 & 4.96 & 80.69 & 6.44 & 84.70 & 6.07 & 81.07 & 5.93 & 82.01 & 7.46 & 82.89 & 5.03 \\ \hline F1 Score & 81.45 & 4.22 & 78.84 & 7.06 & 79.63 & 7.03 & 82.17 & 5.70 & 80.31 & 5.27 & 79.48 & 6.58 & 80.79 & 4.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Results of ELM with IWSO and ELM with other algorithms on the Breast dataset. Classification of the Diabetes dataset by ELM and metaheuristic algorithms is shown in Table 14. It was found that IWSO was 98.83 in the precision values, PSO was 99.03 in the sensitivity values, PSO was 98.00 in the specificity values, and PSO was 97.04 in the F1 score values.