# Improved War Strategy Optimization with Extreme Learning Machine for Health Data Classification
## 1 Introduction  
With the development of technology, data in the field of health is becoming widespread. Improvements are made in patient diagnosis using health data with artificial intelligence algorithms. Models are created to be used in predicting, treating, monitoring, and managing a health-related disease or medical condition. Methods consisting of the processing and training processes of many data, including patients' medical records, medical images, data received from sensors, and simultaneous verbal data, are used. As a result of all this, patients are provided with individualized treatment and care. Thus, the cost of treatment is reduced, and diseases are diagnosed early and accurately. There are improvements in patient results in a short time. It provides the opportunity to follow and monitor the patient simultaneously. It predicts the severity of the disease, death, and survival of the patients and determines the risky patients [1]  
Disease diagnosis classifications in the field of health are made according to the ICD (International Classification of Diseases) guide [2]. Although this classification guide,which defines disease and health-related problems, is instructive, it can be confusing or take time due to its excessive detail. While making the correct diagnosis, the use of artificial intelligence-supported applications makes things much easier and minimizes errors. Correct classification of diseases such as heart diseases, liver diseases, breast cancer, diabetes, which are especially common, in a short time prevents possible disabilities and diseases [3]. It is essential to make successful classifications using artificial intelligence algorithms in important issues such as human life and common diseases. ELM, one of the machine learning methods, stands out compared to ANN and DNN due to its fast convergence capability compared to other algorithms and its ability to be trained on low-dimensional data [4]. ELM (Extreme Learning Machine), which is one of the feedforward neural networks, is a frequently preferred learning algorithm due to its fast and precise classification capability. In ELM, the fact that the input weights are random, that it works faster, that it can generalize faster, and that different activation functions (sigmoid, hardlim, sine, etc.) can be used to provide an advantage by performing better. However, overfitting (overlearning), poor determination of initial weights, incorrect activation function, and hidden layer neuron number choices can negatively affect ELM results by making the performance sensitive [4]. Optimization algorithms are used to improve the classification success of complex, difficult-to-solve, and time-consuming problems. Optimization algorithms are frequently used to solve problems that require long-term solutions in large solution spaces in a reasonable time [5]. In nonparametric data, it is used to determine neighborhood relationships. This method provides optimization by analyzing the proximity of certain data points to each other. In ELM, better learning occurs through optimization of parameters with metaheuristic algorithms. As a result, the diagnosis of the disease can be made successfully by making a more accurate classification. Studies on the health data of common diseases in the world are included in detail in Section 2. The WSO (War Strategy Optimization) algorithm was proposed by [PERSON] et al. based on war strategy [6]. This algorithm can provide more accurate results by making the process of finding solutions more efficient, especially in complex optimization problems. The opposite-based learning strategy is an optimization that provides faster convergence by exploring the opposite solutions of a candidate solution in the search space [7]. Random opposite-based learning strategy can be used to achieve more successful classification of health data with optimization algorithms for more successful exploration of the search space.