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发表时间：2018-01-01

发表刊物：系统工程理论与实践

卷号：38

期号：2

页面范围：532-544

ISSN号：1000-6788

摘要：Prostate cancer is one of the highest incidence of cancer in male. The most effective way to reduce prostate cancer mortality and treat patients is to detect it earlier. So far, the accuracy of early screening of prostate cancer is still unsatisfactory and pathological examinations seriously hurt patients body, as well as the existing cancer diagnosis method based on data mining is only focus on the accuracy or interpretability of diagnostic results. According to these problems, this paper proposes a multi-objective neural network-based diagnostic model. In our approach, feature selection is carried out to extract the most explanatory subset of features, thereby improving the explanatory capability and accuracy of the model. Evolutionary computation is employed to learn the network structure and weights, with which the correlation between clinical information and prostate cancer can be identified for diagnosis of prostate cancer. And the Pareto optimization method is used to optimize the structure and parameters of the model during training process, thus providing a set of effective diagnostic model to meet the different decision-making preferences of medical workers. © 2018, Editorial Board of Journal of Systems Engineering Society of China. All right reserved.

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