# Improved War Strategy Optimization with Extreme Learning Machine for Health Data Classification
## 8 Health Datasets Used  
Eleven frequently used health datasets were obtained from the UCI (University of California, Irvine) repository and the Kaggle platform. The description of the health data is given in Table 1. The datasets used in this study were chosen from the most common diseases in the world. The detailed features of the health datasets used in the study are as follows. Breast Cancer Wisconsin is obtained from the University of Wisconsin Hospitals. It consists of 9 features, 699 instances, and 2 classes, which are benign (non-cancerous) or malignant (cancerous) [30]. Bupa is obtained from the University of California at Irvine. Bupa is a liver disorder. It consists of 5 features, 345 instances, and 2 classes, which are the presence of a liver disorder or absence of a liver disorder [31]. Dermatology is obtained from Bilkent University, Department of Computer Engineering and Information Science, Turkey. It consists of 34 features, 366 instances, and 6 classes, which are psoriasis, seboreic dermatitis,lichen planus, pityriasis rosea, cronic dermatitis, and pityriasis rubra pilaris [32]. Diabetes is obtained from the US National Institute of Diabetes and Digestive and Kidney diseases. All patients are Pima Indian women. It consists of 9 features, 768 instances, and 2 classes, which are healthy and diabetes [33]. Hepatitis is obtained from Carnegie-Mellon University, Yugoslav. Hepatitis is a viral inflammation of the liver. It consists of 19 features, 155 instances, and 2 classes, which are healthy and patient [34]. Lymphography is obtained from the University Medical Centre, Institute of Oncology, Ljubljana, Slovenia. It consists of 19 features, 148 instances, and 4 classes, which are normal find, metastases, malign lymph, and fibrosis [35]. Parkinsons is obtained from the Cleveland Heart and Parkinson. It consists of 22 features, 197 instances, and 2 classes, which are healthy and Parkinson Disease [36]. SAheart is South Africa's heart disease dataset. All patients are men in the high-risk area of the Western Cape, South Africa. It consists of 10 features, 462 instances, and 2 classes, which are healthy and heart disease [37]. SPECTF is obtained from Single Proton Emission Computed Tomography (SPECT) cardiac images. It consists of 44 features, 267 instances, and 2 classes, which are normal and abnormal images [38]. Vertebral is obtained from the Group of Applied Research in Orthopedics (GARO) of the Centre MA@dico-Chirurgical de RA@adaptation des Masses, Lyon, France. It consists of 6 features, 310 instances, and 3 classes, which are Normal, Disk Hernia, and Spondylolisthesis [39]. WDBC is obtained from the Wisconsin Diagnostic Breast Cancer (WDBC) dataset. It consists of 30 features, 569 instances, and 2 classes, which are benign or malignant [40]. In Figure 5, Extreme Learning Machine (ELM) was used to classify health datasets. Metaheuristic algorithms were employed to optimize the weights of ELM's input neurons and the bias parameters of hidden layer neurons. ELM was implemented with a sigmoid activation function in the hidden layer, using 20 hidden neurons adopted from [24; 25]. Only missing data were removed from the datasets. There was no other data preprocessing performed. To ensure robustness, each algorithm was independently run 30 times for each dataset, and before each run trial, the dataset was randomly divided into a 70% training set and 30% test set with paying attention to the class distribution. Thus, the potential for overfitting and underfitting problems is taken into account, and unbiased, generalized results are obtained with 30 random runs. The number of iterations is set to 250, which can be seen as a reasonable number based on the results of the experimental convergence curves. The general hyperparameters for the optimization algorithms were configured as follows: Swarm size is 50, lower bound is \(-1\), upper bound is \(+1\), decision variable size