Implementation of novel pattern recognition approach using data mining for medical applications

Abstract

The capacity of pattern recognition algorithms to analyze complicated data and retrieve useful information has attracted a lot of interest in the world of medical solutions. Data mining methods have become effective tools for pattern detection in the medical field, allowing for the extraction of insightful knowledge from huge and complex datasets. In this study, a diversified convergent squirrel search optimization algorithm and support vector machine (DCSSO-SVM) are combined to provide a unique pattern recognition method for medical applications. The DCSSO algorithm draws its inspiration from squirrels' efficient search-space exploration and exploitation during foraging. The suggested method integrates this algorithm with SVM to enhance feature selection, speed up convergence, and increase classification performance. To assess the effectiveness of the suggested DCSSO-SVM strategy, we assembled a dataset of medical images for this study. Other cutting-edge methods are contrasted with the suggested methodology. The experimental findings show that, regarding the accuracy, precision, f1-score, and recall metrics, the suggested DCSSO-SVM technique performs better than the current methods. It also has great toughness and support, making it appropriate for a variety of medicinal applications.