# Binary Starling Murmutation Optimizer Algorithm to Select Effective Features from Medical Data
## 1 Introduction  
With recent advancements in medical information technology, a huge volume of raw medical data is rapidly generated from different medical resources such as medical examinations, radiology, laboratory tests, mobile health applications, and wearable healthcare technologies [1, 2, 3]. Extracting informative knowledge from these medical data using artificial intelligence and machine learning algorithms can help in faster treatment and significantly reduce patient mortality rates [4, 5]. Application of these algorithms in some diseases such as Diabetes, Heart problems, Hepatitis, and Coronavirus is more commonthan others due to their high epidemic and mortality rates, expensive tests, and the requirement of special experience [6; 7; 8]. One of the main challenges in such disease datasets is the existence of redundant and irrelevant features [9], which can decrease the effectiveness of disease diagnosis systems. In medical data mining and machine learning [10; 11], one of the most crucial preprocessing steps is feature selection, which eliminates redundant and irrelevant features to uncover effective ones. Since there are \(2^{N}\) distinct feature subsets in a dataset with \(N\) features, the feature selection problem is NP-hard [12; 13]. Therefore, evaluating all feature subsets to find effective features is very costly, and if each feature is added to the dataset, then the complexity will be doubled [13; 14]. Filter-based, wrapper-based, and embedded methods are the three main categories of feature selection techniques [15; 16]. The classification algorithm is not involved in filter-based methods, which typically operate based on feature ranking. Wrapper-based methods use a classifier algorithm to evaluate individual candidate subsets of features as opposed to filter-based methods [17; 18]. Embedded methods combine the qualities of filter and wrapper methods, and the feature selection algorithm is integrated as part of the learning algorithm [16]. Many wrapper feature selection methods based on metaheuristic algorithms have been proposed [15; 16] that can effectively solve feature selection problems as an NP-hard problem in a reasonable response time [19; 20]. The main goal of using metaheuristic algorithms is to search the feature space and find near-optimal solutions effectively. Metaheuristic algorithms are recognized as robust problem solvers to solve a variety of problems with different types, such as continuous [21], discrete [22; 23; 24], and constraint [25; 26]. Particle swarm optimization (PSO) [27], ant colony optimization (ACO) [28], differential evolution (DE) [29], cuckoo optimization algorithm (COA) [30], krill herd (KH) [31], social spider algorithm (SSA) [32], crow search algorithm (CSA) [33], grasshopper optimization algorithm (GOA) [34], quantum-based avian navigation optimizer algorithm (QANA) [35] and African vultures optimization algorithm (AVOA) [36] are some of the successful metaheuristic algorithms that are promisingly developed to solve feature selection problems. Many metaheuristic-based methods have been proposed to select features from medical data [37; 38; 39]. However, a few of them can select effective features that may provide acceptable accuracy in diagnosing all the targeted diseases in this study, including Diabetes, Heart problems, Hepatitis, and Coronavirus [40]. The main reason for this drawback is generating and storing many irrelevant and redundant features in the medical processes, which reduces the efficiency of classification algorithms used in disease diagnosis systems. Therefore, a metaheuristic algorithm is needed to select useful and effective features from medical datasets by striking a proper balance between local and global search strategies. Responding to this need, particularly for the datasets targeted in the scope of this study, is our motivation to introduce binary versions of the newly proposed starling murmuration optimizer (SMO) algorithm [41], which can balance between its search strategies efficiently.