Multi-objective neural network-based diagnostic model of prostatic cancer

KONG Qian, WANG Dujuan, WANG Yanzhang, JIN Yaochu, JIANG Bin

Systems Engineering - Theory & Practice ›› 2018, Vol. 38 ›› Issue (2) : 532-544.

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Systems Engineering - Theory & Practice ›› 2018, Vol. 38 ›› Issue (2) : 532-544. DOI: 10.12011/1000-6788(2018)02-0532-13

Multi-objective neural network-based diagnostic model of prostatic cancer

  • KONG Qian1, WANG Dujuan1, WANG Yanzhang1, JIN Yaochu1,2, JIANG Bin3
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Abstract

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.

Key words

prostate cancer diagnosis / multi-objective neural network learning / evolutionary computation / feature selection

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KONG Qian , WANG Dujuan , WANG Yanzhang , JIN Yaochu , JIANG Bin. Multi-objective neural network-based diagnostic model of prostatic cancer. Systems Engineering - Theory & Practice, 2018, 38(2): 532-544 https://doi.org/10.12011/1000-6788(2018)02-0532-13

References

[1] WHO. World cancer report 2014[M]. World Health Organization, 2014.
[2] Wu C, Fang K, Chen T. Applying data mining for prostate cancer[C]//International Conference on New Trends in Information and Service Science. Beijing, 2009:3.
[3] Chen W, Zheng R, Baade P D, et al. Cancer statistics in China, 2015[J]. CA:A Cancer Journal for Clinicians, 2016:115-132.
[4] Schroeder F H, Hugosson J, Roobol M J, et al. Screening and prostate-cancer mortality in a randomized european study[J]. New England Journal of Medicine. 2009, 360(13):1320-1328.
[5] 兰雨, 何秀丽. 经直肠超声引导下穿刺活检在前列腺癌诊断中的临床应用价值[J]. 解放军医学杂志, 2016, 41(5):416-419.Lan Y, He X L. The clinical significance of transrectal ultrasound-guided prostate biopsy in the diagnosis of prostate cancer[J]. Medical Journal of Chinese People's Liberation Army, 2016, 41(5):416-419.
[6] Welch H G, Albertsen P C. Prostate cancer diagnosis and treatment after the introduction of prostate-specific antigen screening:1986-2005[J]. JNCI Journal of the National Cancer Institute, 2009, 101(19):1325-1329.
[7] Bellazzi R, Zupan B. Predictive data mining in clinical medicine:Current issues and guidelines[J]. International Journal of Medical Informatics, 2008, 77(2):81-97.
[8] Zubi Z S, Saad R A. Using some data mining techniques for early diagnosis of lung cancer[C]//WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases. World Scientific and Engineering Academy and Society, 2011:32-37.
[9] Yang H, Chen Y P. Data mining in lung cancer pathologic staging diagnosis:Correlation between clinical and pathology information[J]. Expert Systems with Applications. 2015, 42(15-16):6168-6176.
[10] Yucebas S C, Aydin S Y. A prostate cancer model build by a novel SVM-ID3 hybrid feature selection method using both genotyping and phenotype data from dbGaP[J]. PLoS One, 2014, 9(3):e91404.
[11] 吕冬姣, 张珏, 王霄英, 等. 人工神经网络在前列腺癌诊断中的应用[J]. 北京大学学报(医学版). 2009, 41(4):469-473.Lü D J, Zhang J, Wang X Y, et al. Application of artificial neural network to diagnosis of prostate cancer[J]. Journal of Peking University (Health Sciences), 2009, 41(4):469-473.
[12] 宋敏, 王开正, 杭永伦, 等. 基于人工神经网络的前列腺癌诊断模型对前列腺癌的诊断价值研究[J]. 中国全科医学. 2012, 15(35):4061-4063.Song M, Wang K Z, Hang Y L, et al. Artificial neural network-Based diagnostic model of prostatic cancer[J]. Chinese General Practice, 2012, 15(35):4061-4063.
[13] 李晓峰, 徐玖平, 王荫清, 等. BP人工神经网络自适应学习算法的建立及其应用[J]. 系统工程理论与实践, 2004, 24(5):1-8.Li X F, Xu J P, Wang Y Q, et al. The establishment of self-adapting algorithm of BP neural network and its application[J]. Systems Engineering-Theory & Practice, 2004, 24(5):1-8.
[14] 王芳, 饶运清, 唐秋华, 等. 多目标决策下Pareto非支配解的快速构造方法[J]. 系统工程理论与实践, 2016, 36(2):454-463.Wang F, Rao Y Q, Tang Q H, et al. Fast construction method of Pareto non-dominated solution for multi-objective decision-making problem[J]. Systems Engineering-Theory & Practice, 2016, 36(2):454-463.
[15] 姚旭, 王晓丹, 张玉玺, 等. 特征选择方法综述[J]. 控制与决策, 2012, 27(2):161-166.Yao X, Wang X D, Zhang Y X, et al. Summary of feature selection algorithms[J]. Control and Decision, 2012, 27(2):161-166.
[16] 邬开俊, 鲁怀伟. 采用并行协同进化遗传算法的文本特征选择[J]. 系统工程理论与实践, 2012, 32(10):2215-2220.Wu K J, Lu H W. PCEGA used to solve text feature selection[J]. Systems Engineering-Theory & Practice, 2012, 32(10):2215-2220.
[17] 姚登举, 杨静, 詹晓娟. 基于随机森林的特征选择算法[J]. 吉林大学学报(工学版), 2014, 44(1):137-141.Yao D J, Yang J, Zhang X J. Feature selection algorithm based on random forest[J]. Journal of Jilin University (Engineering and Technology Edition), 2014, 44(1):137-141.
[18] Jin Y, Sendhoff B, Korner E. Rule extraction from compact pareto-optimal neural networks[M]//Multi-objective evolutionary algorithms for knowledge discovery from databases. Springer Berlin Heidelberg, 2008:71-90.
[19] Smith C, Jin Y. Evolutionary multi-objective generation of recurrent neural network ensembles for time series prediction[J]. Neurocomputing, 2014, 143:302-311.
[20] Tang L, Wang X. A hybrid multiobjective evolutionary algorithm for multiobjective optimization problems[J]. IEEE Transactions on Evolutionary Computation, 2013, 17(1):20-45.
[21] Deb K, Goyal M. A combined genetic adaptive search (GeneAS) for engineering design[J]. Computer Science and Informatics, 1996, 26:30-45.
[22] 杨柳, 王钰. 泛化误差的各种交叉验证估计方法综述[J]. 计算机应用研究, 2015(5):1287-1290.Yang L, Wang Y. Survey for various cross-validation estimators of generalization error[J]. Application Research of Computers, 2015(5):1287-1290.
[23] Jin Y, Sendhoff B. Pareto-based multiobjective machine learning:An overview and case studies[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 2008, 38(3):397-415.

Funding

National Natural Science Foundation of China (71533001); Fundamental Research Funds for the Central Universities (DUT15QY32)
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