Automatic breast cancer diagnosis based on hybrid dimensionality reduction technique and ensemble classification.

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Feature selection in the face of high-dimensional data can reduce overfitting and learning time, and at the same time improve the accuracy and efficiency of the system. Since there are many irrelevant and redundant features in breast cancer diagnosis, removing such features leads to more accurate prediction and reduced decision time when dealing with large-scale data. Meanwhile, ensemble classifiers are powerful techniques to improve the prediction performance of classification models, where several individual classifier models are combined to achieve higher accuracy. In this paper, an ensemble classifier algorithm based on multilayer perceptron neural network is proposed for the classification task, in which the parameters (e.g., number of hidden layers, number of neurons in each hidden layer, and weights of links) are adjusted based on an evolutionary approach. Meanwhile, this paper uses a hybrid dimensionality reduction technique based on principal component analysis and information gain to address this problem. The effectiveness of the proposed algorithm was evaluated based on the Wisconsin breast cancer database. In particular, the proposed algorithm provides an average of 17% better accuracy compared to the best results obtained from the existing state-of-the-art methods. Experimental results show that the proposed algorithm can be used as an intelligent medical assistant system for breast cancer diagnosis.