Enhanced otpmization based support vector machine classification approach for the detection of knee arthritis

The accurate detection of knee arthritis is essential for effective medical diagnosis and treatment. In this study, we propose an enhanced classification approach using a support vector machine (SVM) coupled with Cuckoo search optimization (CSO) to improve the detection of knee arthritis. The classification challenge lies in tuning the hyperparameters of the SVM, specifically the penalty parameter (C) and the kernel function parameter (γ), which significantly influence the model’s performance. Traditional methods of hyperparameter tuning may be computationally expensive and prone to local minima. To address these challenges, we integrate CSO as an optimization algorithm for the efficient search of optimal hyperparameters. Cuckoo search optimization, inspired by the brood parasitism behavior of cuckoo birds, is applied to optimize the SVM hyperparameters by balancing exploration and exploitation during the search process. CSO efficiently explores the hyperparameter space and finds an optimal or near-optimal solution by minimizing the classification error. The hybrid approach aims to enhance the predictive accuracy and generalization ability of the SVM model. The proposed CSO-SVM framework is validated on a benchmark knee arthritis dataset, and the experimental results demonstrate a significant improvement in classification performance compared to traditional SVM and other optimization algorithms. The proposed model’s ability to optimize hyperparameters with CSO shows promise in achieving higher accuracy, precision, recall, and F1 score, making it a robust approach for knee arthritis detection.