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  • Bristol M L Emery S M Maycotte P Thorburn A

    2019-10-21

    Bristol, M.L., Emery, S.M., Maycotte, P., Thorburn, A., Chakradeo, S., Gewirtz, D.A., 2013. Autophagy inhibition for chemosensitization and radiosensitization in cancer: do the preclinical data support this therapeutic strategy? J. Pharmacol. Exp. Ther. 344, 544–552.
    Fougeray, S., Pallet, N., 2015. Mechanisms and biological functions of autophagy in diseased and ageing kidneys. Nat. Rev. Nephrol. 11, 34–45. Fu, Y.F., Liu, X., Gao, M., Zhang, Y.N., Liu, J., 2017. PFK-158 stress induces autophagy and apoptosis while inhibiting proliferation and drug resistance in mul-tiple myeloma through the PI3K/Akt/mTOR signaling pathway. Oncotarget 8, 61093–61106.
    Yoshida, G.J., 2017. Therapeutic strategies of drug repositioning targeting autophagy to induce cancer cell death: from pathophysiology to treatment. J. Hematol. Oncol. 10, 67.
    Contents lists available at ScienceDirect
    Information Processing and Management
    journal homepage: www.elsevier.com/locate/infoproman
    A novel intelligent classification model for breast cancer diagnosis
    T
    a College of Management and Economics, Tianjin University, Tianjin, 300072, China b School of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832000, China c Business School, Nankai University, Tianjin, 300071, China d School of Computer Science and Technology, Anhui University, Hefei, 230601, China e Key Laboratory of Artificial Cell, Department of Pathology, The Third Central Hospital of Tianjin Medical University, Tianjin, 300170, China
    Keywords:
    Support vector machine
    Breast cancer diagnosis
    Information gain
    Cost-sensitive learning
    Genetic algorithm 
    Breast cancer is one of the leading causes of death among women worldwide. Accurate and early detection of breast cancer can ensure long-term surviving for the patients. However, traditional classification algorithms usually aim only to maximize the classification accuracy, failing to take into consideration the misclassification costs between different categories. Furthermore, the costs associated with missing a cancer case (false negative) are clearly much higher than those of mislabeling a benign one (false positive). To overcome this drawback and further improving the classification accuracy of the breast cancer diagnosis, in this work, a novel breast cancer in-telligent diagnosis approach has been proposed, which employed information gain directed si-mulated annealing genetic algorithm wrapper (IGSAGAW) for feature selection, in this process, we performs the ranking of features according to IG algorithm, and extracting the top m optimal feature utilized the cost sensitive support vector machine (CSSVM) learning algorithm. Our proposed feature selection approach which can not only help to reduce the complexity of SAGASW algorithm and effectively extracting the optimal feature subset to a certain extent, but Replicative transposition can also obtain the maximum classification accuracy and minimum misclassification cost. The efficacy of our proposed approach is tested on Wisconsin Original Breast Cancer (WBC) and Wisconsin Diagnostic Breast Cancer (WDBC) breast cancer data sets, and the results demonstrate that our proposed hybrid algorithm outperforms other comparison methods. The main objective of this study was to apply our research in real clinical diagnostic system and thereby assist clinical physicians in making correct and effective decisions in the future. Moreover our proposed method could also be applied to other illness diagnosis.
    1. Introduction
    Breast cancer is one of the leading causes of death among women worldwide (Sheikhpour, Ghassemi, Yaghmaei, Ardekani, & Shiryazd, 2014). According to the American Cancer Society (ACS), an estimation of 246,660 women will be diagnosed with breast cancer and approximately 40,450 women will die from this disease in 2016 (American Cancer Society. 2016). In China, there has been an estimated of 214,360 women has died from breast cancer by 2008, and the number of death will reach up to 2.5 million by 2021 (Fan et al., 2014). However, according to the survey, more than 30% of cancer cases will be surviving for a long-time if they accept the accurate early detection (Sizilio, Leite, Guerreiro, & Neto, 2012). Thus, it is imperative for us to design an accurately and