Design of Ensemble Classifier Model Based on MLP Neural Network For Breast Cancer Diagnosis

• Published in: Inteligencia artificial Vol. 24; no. 67; pp. 147 - 156
• Main Authors: Rezaeipanah, Amin, Syah, Rahmad, Wulandari, Siswi, Arbansyah, A
• Format: Journal Article
• Language: English
• Published: Asociación Española para la Inteligencia Artificial 16.07.2021
• Subjects: Breast Cancer Diagnosis; Ensemble Classification; Evolutionary Algorithm; MLP Neural Network; Optimization
• ISSN: 1137-3601; 1988-3064; 1988-3064
• DOI: 10.4114/intartif.vol24iss67pp147-156

Nowadays, breast cancer is one of the leading causes of death women in the worldwide. If breast cancer is detected at the beginning stage, it can ensure long-term survival. Numerous methods have been proposed for the early prediction of this cancer, however, efforts are still ongoing given the importance of the problem. Artificial Neural Networks (ANN) have been established as some of the most dominant machine learning algorithms, where they are very popular for prediction and classification work. In this paper, an Intelligent Ensemble Classification method based on Multi-Layer Perceptron neural network (IEC-MLP) is proposed for breast cancer diagnosis. The proposed method is split into two stages, parameters optimization and ensemble classification. In the first stage, the MLP Neural Network (MLP-NN) parameters, including optimal features, hidden layers, hidden nodes and weights, are optimized with an Evolutionary Algorithm (EA) for maximize the classification accuracy. In the second stage, an ensemble classification algorithm of MLP-NN is applied to classify the patient with optimized parameters. Our proposed IEC-MLP method which can not only help to reduce the complexity of MLP-NN and effectively selection the optimal feature subset, but it can also obtain the minimum misclassification cost. The classification results were evaluated using the IEC-MLP for different breast cancer datasets and the prediction results obtained were very promising (98.74% accuracy on the WBCD dataset). Meanwhile, the proposed method outperforms the GAANN and CAFS algorithms and other state-of-the-art classifiers. In addition, IEC-MLP could also be applied to other cancer diagnosis.