# Improved War Strategy Optimization with Extreme Learning Machine for Health Data Classification
## 10 Results and Discussion
Finally, IWSO is proposed and proved to be better than WSO and OWSO in a difficult problem like ELM classifier optimization. Moreover, due to recent advances in deep learning, IWSO benchmarked against contemporary machine learning and deep Learning methods such as XGBoost, LightGBM, SVM, Neural Network (MLP), and CNN. Experimental studies have repeated 30 trials with the same conditions, as the dataset was split randomly 70% train set and a 30% test set before each trial. The means of the accuracy obtained and compared in Table 6. According to the table, IWSO's superiority in all datasets ranks first, while LightGBM ranks second and XGBoost ranks third in the overall ranking. In the convergence curves results, it was determined that the IWSO was successful in health datasets for 250 iterations (Figure 6). Except for the Dermatology and Lymphography datasets, it has been observed that IWSO achieves a more successful classification accuracy by quickly converging to the global optimum point, as shown in the convergence figure. It was determined that it reached a stable result after the 50 th iteration, especially in the Breast, Bupa, Diabetes, Parkinsons, SAheart, SPECTF, Vertebral, and WDBC datasets. It is determined that it converges successfully after 100 iterations in the Hepatitis dataset. The rapid convergence of IWSO and its competitive advantage in very early iterations shows that it can decisively make the correct classification. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **ALO** & **DA** & **PSO** & **GWO** & **WSO** & **OWSO** & **IWSO** \\ \hline Breast & 7 & 6 & 2 & 4 & 5 & 3 & * 1 \\ \hline Bupa & 6 & 3 & 4 & 2 & 5 & 7 & * 1 \\ \hline Dermatology & 7 & 6 & 4 & 2 & 5 & * 1 & 3 \\ \hline Diabetes & 5 & 4 & 6 & 3 & 7 & 2 & * 1 \\ \hline Hepatitis & 7 & 6 & 2 & 5 & 4 & 3 & * 1 \\ \hline Lymphography & 7 & 3 & * 1 & 2 & 6 & 5 & 4 \\ \hline Parkinsons & 7 & 6 & 2 & 3 & 4 & 5 & * 1 \\ \hline SAheart & 7 & 4 & 3 & 2 & 6 & 5 & * 1 \\ \hline SPECTF & 7 & 6 & 5 & 4 & 2 & 3 & * 1 \\ \hline Vertebral & 6 & 5 & 4 & 2 & 7 & 3 & * 1 \\ \hline WDBC & 7 & 4 & 3 & 2 & 5 & 6 & * 1 \\ \hline Sum of ranks & 73 & 53 & 36 & 31 & 56 & 43 & 16 \\ \hline Mean of ranks & 6.64 & 4.82 & 3.27 & 2.82 & 5.09 & 3.91 & 1.45 \\ \hline Overall ranks & 7 & 5 & 3 & 2 & 6 & 4 & * 1 \\ \hline \hline \end{tabular}  
* Best ranks.  
\end{table} 
Table 5: Results of the Friedman test. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **LightGBM** & **XGBoost** & **SVM** &
\begin{tabular}{c} **Neural** \\ **Network (MLP)** \\ \end{tabular} & **CNN** & **IWSO** \\ \hline Breast & 96.72 & 96.26 & 97.02 & 95.45 & 96.08 & * 98.71 \\ \hline Bupa & 68.88 & 68.46 & 68.37 & 68.27 & 68.11 & * 81.42 \\ \hline Dermatology & 96.57 & 96.30 & 69.51 & 96.91 & 31.48 & * 99.60 \\ \hline Diabetes & 74.29 & 73.19 & 75.09 & 67.95 & 66.67 & * 80.91 \\ \hline Hepatitis & 87.64 & 85.97 & 83.33 & 79.58 & 80.14 & * 99.03 \\ \hline Lymphography & 82.67 & 84.44 & 78.00 & 81.78 & 75.33 & * 94.39 \\ \hline Parkinsons & 90.90 & 88.70 & 79.72 & 78.47 & 79.66 & * 92.76 \\ \hline SAheart & 67.34 & 66.69 & 65.78 & 68.51 & 63.62 & * 79.06 \\ \hline SPECTF & 78.72 & 79.96 & 79.14 & 77.45 & 78.68 & * 87.42 \\ \hline Vertebral & 83.55 & 83.05 & 85.66 & 79.68 & 48.39 & * 90.00 \\ \hline WDBC & 96.41 & 96.34 & 91.83 & 92.34 & 88.64 & * 98.14 \\ \hline Mean of Acc. & 83.97 & 83.58 & 79.40 & 80.58 & 70.62 & * 91.04 \\ \hline Rank & 2 & 3 & 5 & 4 & 6 & * 1 \\ \hline \hline \end{tabular}  
* Best mean accuracy results.  
\end{table} 
Table 6: Accuracy results of IWSO and contemporary machine and deep learning methods. Figure 6: _Cont_. Figure 6: Convergence curves for IWSO and other methods. It is found that the proposed IWSO has fast convergence capability, shows successful classification performance on most of the datasets also outperforms other algorithms and machine learning, deep learning methods. In some datasets, traditional algorithms such as PSO and GWO achieved comparable or even superior performance.