Breast Cancer Prediction Using Genetic Algorithm Based Ensemble Approach

Breast cancer prediction is an open area of research. Breast cancer is a classification problem which can be solved by machine learning models like a decision tree, random forest, support vector machine, and many more models. Each machine learning model has its own merits and demerits. In breast cancer prediction we need to improve the accuracy of models, so we use here ensemble method which combines predictions of multiple models. An ensemble is a method to increase the prediction accuracy of breast cancer. In this study, a new technique is introduced to GA based weighted average ensemble method of classification dataset which overcame the limitations of the classical weighted average method. Genetic algorithm based weighted average method is used for the prediction of multiple models. The comparison between Particle swarm optimization(PSO), Differential evolution(DE) and Genetic algorithm(GA) and it is concluded that the genetic algorithm outperforms for weighted average methods. One more comparison between classical ensemble method and GA based weighted average method and it is concluded that GA based weighted average method outperforms.