Combining ensemble classification and integrated filter-evolutionary search for breast cancer diagnosis.

Abstract

Breast cancer is one of the most common chronic diseases and the second cause of death among women, where its timely diagnosis plays an important role in survival and treatment. Advances in technology have led to the emergence of computerized diagnostic systems as intelligent medical assistants. In recent years, the development of these systems with data mining techniques and machine learning approaches has attracted the attention of researchers. This study presents a new hybrid approach using data mining techniques including feature selection and classification. Feature selection is configured using a method based on integrated filter-evolutionary search, where this method includes an evolutionary algorithm and information gain. The proposed feature selection method can provide the most suitable features by reducing dimensions for breast cancer classification. Meanwhile, we introduce an ensemble classification approach based on neural networks whose parameters are adjusted by an evolutionary algorithm. The effectiveness of the proposed method has been evaluated by several real datasets from the UCI machine learning repository. The results of simulations in terms of various metrics such as accuracy, precision and recall show that the proposed method is 12% better than the best existing methods on average. The evaluation of the proposed method confirms its effectiveness for breast cancer diagnosis as an intelligent medical assistant.