# Improved War Strategy Optimization with Extreme Learning Machine for Health Data Classification
## 10 Results and Discussion  
Breast, Bupa, Dermatology, Diabetes, Hepatitis, Lymphography, Parkinsons, SAheart, SPECTF, Vertebral, WDBC health data were classified as a result of ELM's parameter optimization with the help of metaheuristic algorithms. ALO, DA, PSO, GWO, WSO, OWSO, and IWSO algorithms were used in the health data. Accuracy and standard deviation (Std.) in Tables 2 and 3, precision, sensitivity, specificity, F1 score mean percentage and standard deviation (Std.) results of the test data were calculated in Appendix A. Tables A2-A12. The fact that the standard deviation is generally low in all tables means that the data gives results close to the calculated average success result. The best mean accuracy results are shown with an asterisk mark (*) in the tables. The asterisk mark (*) is used to make the winner more prominent. According to the results of classification accuracy, IWSO is best in Breast, Bupa, Diabetes, Hepatitis, Parkinsons, SAheart, SPECTF, Vertebral, WDBC datasets with 98.71, 81.42, 80.91, 99.03, 92.76, 79.06, 87.42, 90.00, 98.14 values, respectively. First, to see the performance effects of the opposition and random opposition-based strategies, the classification accuracy of standard WSO, opposition-based OWSO, and random opposition-based IWSO is compared on eleven datasets. A comparative analysis is given in Table 2 that IWSO is superior to OWSO and OWSO is superior to WSO under identical settings to show the specific performance gain attributable to the randomized opposition mechanism. According to the overall rank, IWSO is in first place with 91.04% and OWSO is in second place with 89.22%, and WSO is in last place with 88.45%. IWSO, the winner of the Table 3 comparative accuracy analysis, is compared with classification accuracies of ALO, DA, PSO, and GWO in Table 3. Accordingly, IWSO ranked first with an overall mean classification accuracy of 91.04% and it is followed by GWO, PSO, DA, and ALO with 89.77%, 89.09%, 88.47%, and 87.40% accuracy values, respectively. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**WSO**} & \multicolumn{2}{c}{**OWSO**} & \multicolumn{2}{c}{**IWSO**} \\ \cline{2-7}  & **Mean** & **Std.** & **Mean** & **Std.** & **Mean** & **Std.** \\ \hline Breast & 98.42 & 0.76 & 98.56 & 0.79 & * 98.71 & 0.62 \\ \hline Bupa & 78.93 & 3.60 & 78.67 & 3.34 & * 81.42 & 3.24 \\ \hline Dermatology & 97.41 & 2.22 & * 99.66 & 0.52 & 99.60 & 0.59 \\ \hline Diabetes & 74.67 & 1.82 & 79.52 & 3.28 & * 80.91 & 2.04 \\ \hline Hepatitis & 97.78 & 2.62 & 97.92 & 2.38 & * 99.03 & 1.79 \\ \hline Lymphography & 93.33 & 2.73 & 94.09 & 2.20 & * 94.39 & 2.51 \\ \hline Parkinsons & 88.74 & 2.67 & 88.56 & 2.85 & * 92.76 & 1.72 \\ \hline SAheart & 75.02 & 2.09 & 75.39 & 2.05 & * 79.06 & 2.60 \\ \hline SPECTF & 86.63 & 2.83 & 86.33 & 2.27 & * 87.42 & 2.95 \\ \hline Vertebral & 87.38 & 2.07 & 88.35 & 2.98 & * 90.00 & 2.04 \\ \hline WDBC & 94.59 & 1.48 & 94.39 & 1.37 & * 98.14 & 1.05 \\ \hline Overall Mean & 88.45 & & 89.22 & & * 91.04 & \\ \hline Overall Rank & 3 & & 2 & & 1 \\ \hline \hline \end{tabular}  
* Best mean accuracy results.  
\end{table} 
Table 2: Comparative accuracy analysis of WSO, OWSO, and IWSO. Results of statistical significance Wilcoxon rank sum test [28] with a significance level of 5%, are presented in Table 4. The null hypothesis is accepted (when a \(p\) value is greater than 0.05 and hi is -) and indicates that there is no difference between the two algorithms. Otherwise, the null hypothesis is rejected; thus, the \(p\) value is lower than 0.05, and h is +. Results of the statistical significance test indicate that the proposed IWSO method is significantly different from all other algorithms. The Friedman test [30] is used to show how the proposed IWSO and other algorithms' accuracy success performs overall. The Friedman test ranks algorithms according to their performance on each dataset independently.