Medical datasets are primarily made up of numerous pointless and redundant elements in a collection of patient records. None of these characteristics are necessary for a medical decision-making process. Conversely, a large amount of data leads to increased dimensionality and decreased classifier performance in terms of machine learning. Numerous approaches have recently been put out to address this issue, and the results indicate that feature selection can be a successful remedy. To meet the various needs of input patterns, medical diagnostic tasks typically involve learning a suitable categorization model. The k-Nearest Neighbors algorithm (kNN) classifier's classification performance is typically decreased by the input variables' abundance of irrelevant features. To simplify the kNN classifier, essential attributes of the input variables have been searched using the feature selection approach. This paper presents the Coati Optimization Algorithm (DCOA) in a dynamic form as a feature selection technique where each iteration of the optimization process involves the introduction of a different feature. We enhance the exploration and exploitation capability of DCOA by employing dynamic opposing candidate solutions. The most impressive feature of DCOA is that it does not require any preparatory parameter fine-tuning to the most popular metaheuristic algorithms. The CEC'22 test suite and nine medical datasets with various dimension sizes were used to evaluate the performance of the original COA and the proposed dynamic version. The statistical results were validated using the Bonferroni-Dunn test and Kendall's W test and showed the superiority of DCOA over seven well-known metaheuristic algorithms with an overall accuracy of 89.7%, a feature selection of 24%, a sensitivity of 93.35% a specificity of 96.81%, and a precision of 93.90%.
Keywords: Coati optimization algorithm; Dynamic opposite; Feature selection; Machine learning; Medical diagnostic; k-nearest neighbor.
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