Toward improving the performance of learning by joining feature selection and ensemble classification techniques: an application for cancer diagnosis.

Abstract

Breast cancer is known as the most common type of cancer in women, and this has raised the importance of its diagnosis in medical science as one of the most important issues. In addition to reducing costs, the diagnosis of benign or malignant breast cancer is very important in determining the treatment method. The purpose of this paper is to present a model based on data mining techniques including feature selection and ensemble classification that can accurately predict breast cancer patients in the early stages. The proposed breast cancer detection model is developed by joining Adaptive Differential Evolution (ADE) algorithm for feature selection and Learning Vector Quantization (LVQ) neural network for classification. Our proposed model as ADE-LVQ has the ability to automatically and quickly diagnose breast cancer patients into two classes, benign and malignant. As a new evolutionary approach, ADE performs optimal configuration for LVQ neural network in addition to selecting effective features from breast cancer data. Meanwhile, we configure an ensemble classification technique based on LVQ, which significantly improves the prediction performance. ADE-LVQ has been analyzed from different perspectives on different datasets from Wisconsin breast cancer database. We apply different approaches to handle missing values and improve data quality on this database. The results of the simulations showed that the ADE-LVQ model is more successful than the equivalent and state-of-the-art models in diagnosing breast cancer patients. Also, ADE-LVQ provides better performance with less complexity, considering feature selection and ensemble learning. In particular, ADE-LVQ improves accuracy (up to 3.4%) and runtime (up to 2.3%) on average compared to the existing best method. Combined methods based on data mining techniques for breast cancer diagnosis can help doctors in making better decisions for disease treatment.