# Improved War Strategy Optimization with Extreme Learning Machine for Health Data Classification
## 2 Literature Review  
In recent years, the classification of medical datasets has been significantly enhanced through the integration of machine learning algorithms with optimization techniques. Particularly, Extreme Learning Machine (ELM), due to its fast training speed and high generalization capability, has been widely adopted in various health-related classification problems. However, ELM's sensitivity to parameter initialization has necessitated the use of metaheuristic optimization methods to improve its performance. Various algorithms have been employed in the literature to enhance ELM-based classification for specific datasets. For the Breast Cancer dataset, high accuracy rates have been reported using different approaches: Flower Pollination Algorithm (FPA) and Gray Wolf Optimization (GWO) achieved 97.81% [8]; Radial Basis Function (RFFs) and Radial Basis Function Neural Networks-ELM-Genetic Algorithm (RBFNN-ELM-GA) attained 97.38% [9]; and Differential Evolution (DE) reached 98.74% [10]. For the Bupa dataset, accuracies include 88.90% for Hybrid Activation-based ELM (HAELM) [11], 76.75% for Artificial Bee Colony (ABC) [12], and 88.36% for ELM [13]. In the classification of the Dermatology dataset, the combination of Factor Analysis (FA) with ELM achieved 100% accuracy [14], while Principal Component Analysis-Kernel ELM (PCA-KELM) reached 95.60% [15]. In the Diabetes dataset, accuracy rates of 78.62% with Particle Swarm Algorithm (PSO) [8], 77.61% with RBF and RBFNN-ELM-GA [9], and 79.69% with ELM [10] have been reported. For the Hepatitis dataset, RBFNN-ELM-GA achieved 87.10% accuracy [9], ELM yielded 63.54% [15], and Improved Cuckoo Search-Based ELM (ICSELM) achieved 100% [13]. For the Lymphography dataset, the NEURO-FUZZY algorithm attained an accuracy of 89.19% [15]. Several notable studies have demonstrated the impact of optimization strategies on ELM and related classifiers. [PERSON] and [PERSON] proposed a PSO-optimized MLP model for heart disease prediction, achieving 84.61% accuracy on the Cleveland dataset, outperforming several traditional classifiers such as SVM and Random Forest [16]. [PERSON] et al. developed a GWO-ELM model for the detection of diabetic retinopathy using HOG and PCA, reporting up to 99.47% accuracy on APTOS-2019 and IDRiD datasets [17]. [PERSON] et al. [18] proposed a lightweight fuzzy SZGWO-ELM neural network model that combines an Extreme Learning Machine (ELM) with an improved Grey Wolf Optimization (GWO) algorithm. The SZGWO-ELM model was evaluated on five UCI medical datasets and achieved high performance with accuracy (96.08%), sensitivity (94.14%), specificity (99.26%), and precision (99.52%). [PERSON] et al. (2024) [19] proposed a method to enhance the performance of Extreme Learning Machine (ELM) classifiers by tuning both the hidden layer neuron count and the initial weights/biases using an improved Firefly Algorithm (FA), named Group Search Firefly Algorithm (GSFA). The proposed ELM-GSFA model was evaluated on 16 benchmark datasets, including both real and synthetic medical datasets, and outperformed nine other metaheuristic-tuned ELM models. Notably, it achieved 84.85% accuracy on the Diabetes dataset, 98.7% on the heart disease dataset, and 83.7% on the Cardiotocography-10 dataset. The results demonstrated that GSFA significantly improves classification accuracy and robustness, particularly on imbalanced and multiclass datasets. [PERSON] et al. (2021) [20] proposed a cloud-based framework for breast cancer diagnosis that leverages the Extreme Learning Machine (ELM) classifier in combination with gain ratio-based feature selection. Using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset, the cloud-based ELM achieved high accuracy (98.68%), recall (91.30%), precision (90.54%), and F1 score (81.29%), highlighting its potential as a rapid and scalable diagnostic tool for remote healthcare applications.