A Homogeneous Ensemble Classifier for Breast Cancer Detection Using Parameters Tuning of MLP Neural Network

Breast cancer is one of the most common cancers among worldwide, and its detection is recognized as a significant public health problem in today’s society. Extensive studies have been conducted to classify patients into malignant or benign groups, but given the importance of the problems, efforts are still ongoing. This paper aims are to parameters tuning of Multi-Layer Perceptron (MLP) neural network for the breast cancer detection. This work presents an MLP-based homogeneous ensemble approach for classifying breast cancer samples. Basically, ensemble learning is used to improve the classification process. This technique is a method of combining different basic classifiers from which a new classifier is derived. In this regard, several optimization algorithms including GA, PSO, and ODMA have been used to determine which algorithm provides the most suitable parameters for MLP. These parameters include effective features, number of hidden layers, number of nodes in layers, and weight values. The proposed algorithm is applied to three datasets of the Wisconsin Breast Cancer Database (i.e., WBCD, WDBC, and WPBC) and then comparison is made between different algorithms to achieve the highest accuracy. Experiments show that the proposed classifier has promising results in breast cancer detection than other state-of-the-art classifiers with 98.79% in the WBCD. Data analysis and its results can confirm the superiority of ensemble classifiers over state-of-the-art methods for breast cancer detection.