Improved Whale optimization Algorithm for SVM Model Selection: Application in Medical Diagnosis

Support-Vector Machines (SVM) have gained momentum and have become a widely used classifier for various applications. Indeed, SVM parameters have a significant impact on the accuracy of the prediction model. The present work aims to propose an improved version of the Whale optimization Algorithm (WOA) that can choose the best model for SVM by seeking the optimal parameter values. To surpass the premature convergence problem and outperform the exploitation ability of the original WOA, two improvements are introduced. First, we increase the potential of the best solution, which redirects the search agent toward the global optimum. Second, a new search equation is added to decrease the overflow of diversity. Experimental results proved that the proposed Improved Whale optimization Algorithm (IWOA) is efficient not only in terms of quality of the final solution and convergence rate for optimization benchmarking function but also for reaching the optimal values of the SVM parameters. In this sense, the study has provided a medical diagnosis-oriented problem, classifying medical, i.e., cancer and diabetes, datasets, to demonstrate the effectiveness of the IWOA-optimized SVM.