Improved War Strategy Optimization with Extreme Learning Machine for Health Data Classification - İbrahim Berkan Aydilek; Arzu Uslu; Cengiz Kına (Applied Sciences)
Classification of diseases is of great importance for early diagnosis and effective treatment processes. However, etiological factors of some common diseases complicate the classification process. Therefore, classification of health datasets by processing them with artificial neural networks can play an important role in the diagnosis and follow-up of diseases. In this study, disease classification performance was examined by using Extreme Learning Machine (ELM), one of the machine learning methods, and an opposition-based WSO algorithm with a random opposite-based learning strategy is proposed. Common health datasets: Breast, Bupa, Dermatology, Diabetes, Hepatitis, Lymphography, Parkinsons, SAheart, SPECTF, Vertebral, and WDBC are used in the experimental studies. Performance evaluation was made by accuracy, precision, sensitivity, specificity, and F1 score metrics. The proposed IWSO-based ELM model has demonstrated better classification success compared to the ALO, DA, PSO, GWO, WSO, OWSO metaheuristics, and LightGBM, XGBoost, SVM, Neural Network (MLP), CNN machine and deep learning methods. In the Wilcoxon test, it was determined that IWSO was p < 0.05 when compared to other algorithms. In the Friedman test, it was determined that IWSO was first in the ranking of success compared to other algorithms. The results reveal that the IWSO approach developed with ELM is an effective method for the accurate diagnosis of common diseases.