Archives

  • 2019-10
  • 2019-11
  • 2020-03
  • 2020-07
  • 2020-08
  • 2021-03
  • br Our future research will focus on

    2020-03-24


    Our future research will focus on the following important issues. First, it would be valuable to verify the proposed model on a larger real dataset from more data sources. Secondly, it would be of interest to apply the proposed method to prognosis prediction of other cancers with a high incidence and high mortality such as lung cancer and prostate cancer. Thirdly, it would be of interest to use additional Dalbavancin advanced machine learning methods other than ensemble classifiers and regressors for the prediction of survivability, metastasis and recurrence and developing better medical decision-making tools.
    Acknowledgment
    References
    [1] J.J. Aguilera, M. Chica, M.J.D. Jesus, et al., Niching genetic feature selection algorithms applied to the design of fuzzy rule-based classification systems, in: Proceedings of the IEEE International Conference on Fuzzy Systems, 2007, pp. 1–6.
    [2] A. Ali, A. Tufail, U. Khan, et al., A survey of prediction models for breast cancer survivability, in: Proceedings of the International Conference on
    [6] C.S. Carvalho, D.S. Souza, J.R. Lopes, et al., Relationship between patient-generated subjective global assessment and survival in patients in palliative care, Ann. Palliat. Med. 6 (2017) 303-303.
    [12] H. Elghazel, A. Aussem, F. Perraud, Trading-off Dalbavancin and accuracy for optimal ensemble tree selection in random forests, in: O. Okun, G. Valentini,
    [13] P. Gao, Y.X. Song, Z.N. Wang, et al., Is the prediction of prognosis not improved by the seventh edition of the TNM classification for colorectal cancer? Analysis of the surveillance, epidemiology, and end results (SEER) database, BMC Cancer 13 (1) (2013) 1–6.
    [16] S. Gu, Y. Jin, Generating diverse and accurate classifier ensembles using multi-objective optimization, in: Proceedings of the IEEE Computational Intelligence in Multi-Criteria Decision-Making, 2015, pp. 9–15.
    [19] U. Khan, H. Shin, J.P. Choi, et al., wFDT: weighted fuzzy decision trees for prognosis of breast cancer survivability, in: Proceedings of the Australasian Data Mining Conference, 2008, pp. 141–152.
    [23] Y.Q. Liu, C. Wang, L. Zhang, Decision tree based predictive models for breast cancer survivability on imbalanced data, in: Proceedings of the IEEE International Conference on Bioinformatics and Biomedical Engineering, 2009, pp. 1–4.
    [26] P. Miksovsky, K. Matousek, Z. Kouba, Data pre-processing support for data mining, in: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, 2002, pp. 208–212.
    [29] D.W. Opitz, Feature selection for ensembles, in: Proceedings of the Sixteenth National Conference on Artificial Intelligence and the Eleventh Innovative Applications of Artificial Intelligence Conference Innovative Applications of Artificial Intelligence, 1999, pp. 379–384.
    [34] V.F. Rodriguez-Galiano, B. Ghimire, J. Rogan, et al., An assessment of the effectiveness of a cytokinesis random forest classifier for land-cover classification, ISPRS
    [38] Surveillance, Epidemiology, and End Results (SEER) Program (www.seer.cancer.gov) Research Data (1973–2013), National Cancer Institute, DCCPS, Surveillance Research Program, Surveillance Systems Branch, released April 2016, based on the November 2015 submission.
    [39] J. Thongkam, G. Xu, Y. Zhang, et al., Breast cancer survivability via AdaBoost algorithms, in: Proceedings of the Australasian Workshop on Health Data and Knowledge Management, 2008, pp. 55–64.
    [46] D. Yao, J. Yang, X. Zhan, Predicting breast cancer survivability using random forest and multivariate adaptive regression splines, in: Proceedings of the IEEE International Conference on Electronic and Mechanical Engineering and Information Technology, 2011, pp. 2204–2207.