## 2 Background Studies
### Nature-Based Algorithms in Feature Selection Used in Different Studies
This review article provided an overview of the feature selection techniques used in ML applications for research on the human microbiome. Wrapper feature selection grey wolf optimization and PSO were combined with a new binary variation proposed in [23]. The best answers were discovered using the K-NN classifier with Euclidean separation matrices. Twenty datasets were used for the tests, and statistical analyses were conducted to evaluate the performance and efficacy of the suggested model using metrics including the ratio of selected features, classification precision, and computing time. In contrast to other algorithms' accuracy levels of 81.6 and 86.8, the average accuracy was 90%. To address the issue of feature selection, the authors of [24] also proposed a brand-new, quick conditional mutual information feature selection algorithm. The experimental results demonstrate the viability of the suggested feature selection technique for constructing a high-level intelligent system to detect heart disease using a classifier SVM. In [25], binary variations of the recently developed grasshopper optimisation algorithm (GOA) were suggested and used to choose the best feature subset for classification in a wrapper-based framework. The comparative results demonstrate the improved performances of the binary GOA and binary GOA-M approaches when compared to similar techniques in the literature. The suggested binary GOA (specifically binary GOA-M) has strengths among the present feature selection algorithms and should be taken into consideration when tackling difficult feature selection problems according to the results, discussions, and assessments. The WOA, the artificial bee colony optimization (BCO) algorithm, and the PSO algorithm were the three algorithms that the authors of [9] identified as having the highest accuracy and having the potential to be used with their dataset to provide accurate diagnoses and potential treatments. The authors of [26] introduced the chaotic crow search algorithm (CCSA), a novel metaheuristic optimizer, to address the issues of low convergence rate and entrapment in local optima. According to experimental findings, CCSA can identify an ideal feature subset that maximizes classification performance while reducing the number of selected features. The experimental findings demonstrate that in terms of the best and mean fitness values, CCSA surpasses the other algorithms. The extended wrapper-based feature selection method described in [27] is based on a parallel novel intelligent GA. The outcomes show that the proposed model is capable of greatly generalizing the proposed multipopulation intelligent GA for datasets with two or more classes. By lowering the number of features from 56 to 28, 34 to 18, 279 to 135, 30 to 16, and 19 to 9 under lung cancer, dermatology, arrhythmia, and hepatitis, respectively, the researchers were able to achieve average classification accuracies of 95.83%, 97.62%, 99.02%, and 98.51%, respectively. In [28], researchers compared and analyzed several nature-inspired algorithms to choose the best traits and factors for distinguishing impacted patients from others. The experimental results demonstrate that the binary bat algorithm beat conventional methods, with a competitive recognition rate on the dataset of chosen features, including PSO, GA, and the modified cuckoo search algorithm.