ABSTRACT

Nowadays, breast cancer is one of the leading causes of women’s death in the world. If breast cancer is detected at the initial stages, it can ensure long-term survival. Numerous methods have been proposed for the early prediction of such cancer. However, efforts are still ongoing, given the importance of the problem. Artificial Neural Networks (ANN) are a prevalent machine learning algorithm, which is very popular for prediction and classification problems. 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 consists of two stages: parameters optimisation and ensemble classification. In the first stage, the MLP Neural Network (MLP-NN) parameters, including optimal features, hidden layers, hidden nodes and weights, are optimised with the help of an Evolutionary Algorithm (EA), aiming at maximising the classification accuracy. In the second stage, an ensemble classification algorithm of MLP-NN with optimised parameters is applied to classify the patients. Our proposed IEC-MLP method not only reduces the complexity of MLP-NN and effectively selects the optimal subset of features but also minimises the misclassification cost. The classification results have been evaluated using the IEC-MLP over different breast cancer datasets, and the prediction results have been auspicious (98.74% accuracy on the WBCD dataset). It is noteworthy that the proposed method outperforms the GAANN and CAFS algorithms and other state-of-the-art classifiers. In addition, IEC-MLP is also capable of being employed in diagnosing other cancer types.

摘要

如今，乳腺癌已成为全球女性死亡的主要原因之一。如果在初始阶段检测到乳腺癌，则可以确保长期生存。已经提出了许多用于早期预测这种癌症的方法。然而，鉴于问题的重要性，努力仍在进行中。人工神经网络 (ANN) 是一种流行的机器学习算法，在预测和分类问题上非常流行。在本文中，提出了一种基于多层感知器神经网络（IEC-MLP）的智能集成分类方法用于乳腺癌诊断。所提出的方法包括两个阶段：参数优化和集成分类。在第一阶段，MLP神经网络（MLP-NN）参数，包括最优特征、隐藏层、隐藏节点和权重，在进化算法（EA）的帮助下进行了优化，旨在最大化分类精度。在第二阶段，应用具有优化参数的 MLP-NN 集成分类算法对患者进行分类。我们提出的 IEC-MLP 方法不仅降低了 MLP-NN 的复杂性并有效地选择了最优的特征子集，而且还最小化了误分类成本。使用 IEC-MLP 对不同乳腺癌数据集的分类结果进行了评估，预测结果很好（在 WBCD 数据集上准确率为 98.74%）。值得注意的是，所提出的方法优于 GAANN 和 CAFS 算法以及其他最先进的分类器。此外，IEC-MLP 还能够用于诊断其他癌症类型。