# Improved War Strategy Optimization with Extreme Learning Machine for Health Data Classification
## 11 Conclusions  
In this study, the classification of health datasets that are widely known in the literature was performed with ELM. A recent metaheuristic algorithm, WSO, was used for optimization of ELM to achieve better classification ability. The opposition-based learning strategy was used with WSO; moreover, OBL was improved with a random strategy. The novel proposed method is named IWSO. When the experimental results were evaluated, it was seen that IWSO showed better classification performance than ALO, DA, PSO, GWO, WSO metaheuristic algorithms, and LightGBM, XGBoost, SVM, Neural Network (MLP), CNN machine and deep learning methods. It was seen that IWSO results were successful in accuracy, precision, sensitivity, specificity, F1 score metrics, and ranked first in the Friedman test compared to other metaheuristic algorithms. In the statistical significance Wilcoxon test, it showed a significant superiority over other algorithms. In addition, it was determined that IWSO converged faster towards the optimum solution in the convergence curves. The proposed IWSO method has been validated as an effective and reliable solution for health data classification. Given its superior performance, it is suggested that IWSO be integrated into health systems. This approach has the potential to support clinical decision-making processes and enhance the success of healthcare professionals in disease classification. Furthermore, incorporating IWSO into medical artificial intelligence algorithms and health informatics systems could facilitate easier and more reliable access to accurate diagnoses for both healthcare providers and patients, as well as their families. In future studies, IWSO is recommended to undergo further validation with real-world, large-scale, or imbalanced datasets, and it can also be used in the context of parameter optimization for machine learning and deep learning algorithms or computationally intensive numerical problems. The limitation of the proposed study is that the IWSO-ELM framework was evaluated only on a selected common set of benchmark health datasets. Its effectiveness on larger, more diverse, and unprocessed real-world datasets--particularly those with class imbalance or noise--remains to be explored. Additionally, the algorithm shows reduced performance on multi-class problems, likely due to the fixed structure of ELM and the war strategy optimizer's convergence ability, and the increased complexity of decision boundaries. Future work should focus on testing the model in more challenging environments to assess its generalizability and robustness.  
**Author Contributions:** Conceptualization, I.B.A., A.U. and C.K.; methodology, I.B.A., A.U. and C.K.; software, I.B.A. and C.K.; supervision, I.B.A.; validation, I.B.A., A.U. and C.K.; writing--review and editing, I.B.A., A.U. and C.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** All datasets included in this work are public datasets.  
**Conflicts of Interest:** The authors declare no conflicts of interest.