AN EFFICIENT BREAST CANCER RECURRENCE PREDICTION USING SIMULATED ANNEALING INERTIA WEIGHT-BASED CHICKEN SWARM OPTIMIZATION ALGORITHM WITH WEIGHTED MINKOWSKI RADIAL BASIS FUNCTION-BASED SUPPORT VECTOR MACHINE

Nowadays the more often diagnosed type of cancer in women is breast cancer. Approximately 12 percent of females worldwide are afflicted by it. Recurrent breast cancer refers to breast cancer that recurs despite having been successfully treated. Breast cancer recurrence is one of the most feared outcomes for cancer patients. As a result, their quality of life is impacted. Even though early-stage breast cancer prediction has always been a difficult research challenge, data mining algorithms can be of considerable assistance in addressing this issue. The prior method used naive Bayes, REPTree, and K-nearest neighbor, Particle Swarm Optimization (PSO) based feature selection for breast cancer recurrence prediction. It is simple for the Particle Swarm Optimization (PSO) method to fall into local optimum in high-dimensional space and has a poor rate of convergence in the iterative process. Furthermore, because of the Nave Bayes’ class conditional independence and the resulting loss of accuracy, it is not recommended. To deal with this problem, the proposed system devised a Simulated Annealing Inertia Weight-based Chicken Swarm Optimization (SAIWCSO) algorithm with a Weighted Minkowski Radial Basis Function-based Support Vector Machine (WMRBF-SVM) for the diagnosis of the reputation of breast cancer. The WBCD is used as an initial source of data. Z-score normalization is then used to ensure that the data is in a consistent format. An algorithm called SAIWCS selects the best features. It enhances the categorization accuracy. To improve the high detection rate, a Weighted Minkowski Radial Basis Function-based Support Vector Machine (WMRBF-SVM) is used to diagnose breast cancer. Python is being used to model the experiments. The experimental findings reveal that the suggested system outperforms the present system with regard to the accuracy, precision, recall, specificity, and f-measure.