# Improved War Strategy Optimization with Extreme Learning Machine for Health Data Classification
## Appendix A Results of Classification Precision, Sensitivity, Specificity, F1 Score Values
In the studies conducted with this dataset, when the accuracy results of IWSO are compared, it is seen that it is higher than NEURO-FUZZY [15], that is, it is more successful in classification. It is thought that the dataset is not open to paper-based learning. Classification of the Parkinsons dataset by ELM and metaheuristic algorithms is shown in Table 14. It was found that OWSO was 94.81 in the precision values, IWSO was 95.17 in the sensitivity values, OWSO was 94.85 in the specificity values, and OWSO was 90.69 in the F1 score values. In the studies conducted with these data, when the accuracy results of IWSO were compared, they were found to be different from RBFs and RBFN-ELM-GA [9], CSO-ELM [10], and ABC [12]. It is seen to be higher; that is, it is more successful in classification. It is seen that the sensitivity results of the IWSO are close to the RBFs and RBFN-ELM-GA [9], but they are lower. In addition, it is seen that the specificity results of IWSO are higher than RBFs and RBFN-ELM-GA [9]. The classification of the SAheart dataset by ELM and metaheuristic algorithms is shown in Table 15. It was found that WSO was 83.35 in the precision values, GWO was 78.54 in the sensitivity values, GWO was 82.79 in the specificity values, and WSO was 76.49 in the F1 score. In the studies conducted with these data, when the accuracy results of IWSO are compared, it is seen that it is higher than DE and MNHO [8] and ELM [10], that is, it is more successful in classification. It is seen that the sensitivity results of IWSO are higher than DE and MNHO [8], and the specificity results are lower than DE [8]. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Parkinsons**} & \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.** & **Mean** & **Std.** \\ \hline Precision & 90.29 & 18.13 & 88.85 & 17.67 & 87.76 & 19.81 & 91.47 & 15.84 & 93.70 & 14.33 & 94.81 & 11.58 & 84.63 & 15.12 \\ \hline Sensitivity & 88.12 & 5.29 & 89.93 & 5.76 & 89.93 & 4.36 & 88.06 & 4.08 & 88.60 & 4.24 & 88.02 & 4.49 & 95.17 & 4.03 \\ \hline Specificity & 92.06 & 7.34 & 92.82 & 6.40 & 92.02 & 7.17 & 93.77 & 5.83 & 93.82 & 7.30 & 94.85 & 6.58 & 93.40 & 3.91 \\ \hline F1 Score & 87.79 & 10.80 & 87.85 & 9.17 & 87.46 & 12.14 & 88.95 & 9.43 & 90.19 & 8.61 & 90.69 & 6.11 & 88.62 & 7.66 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Results of ELM with IWSO and ELM with other algorithms on the Yamphography dataset. Classification of the SPECTF dataset by ELM and metaheuristic algorithms is shown in Table 10. It was found that ALO was 96.19 in the precision values, IWSO was 88.56 in the sensitivity values, GWO was 91.35 in the specificity values, and ALO was 89.72 in the F1 score. In the studies conducted with this dataset, when the accuracy results of IWSO are compared, it is seen that it is higher than GWO [8] and DE [10], that is, it is more successful in classification. It is seen that the sensitivity results of the IWSO are higher than the GWO [8], and the specificity results are lower than the GWO [8]. Classification of the Vertebral dataset by ELM and metaheuristic algorithms is shown in Table 11. It was found that GWO has 88.89 in the precision values, IWSO was 83.59 in the sensitivity values, GWO has 94.72 in the specificity values, and IWSO has 85.87 in the F1 score. In the studies conducted with these data, when the accuracy and specificity results of IWSO are compared, it is seen that it is lower than GWO [8]. The sensitivity results of IWSO are lower than those of GWO [8].