Decision System for the Selection of the Best Therapeutic Protocol for Breast Cancer Based on Advanced Data-Mining: A Survey

  • Sarah KhrouchEmail author
  • Mostafa Ezziyyani
  • Mohammed Ezziyyani
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 914)


Sometimes the experience of Doctors is not enough sufficient to guide patients perfectly and predict exactly the best treatments to follow and give results with high accuracy. For this reason, it is very important to get a predictive model, resulting in effective and accurate decision making. Our main goal is to make a significant contribution toward improving the quality of healthcare. This work strives to create a dynamic graph of treatments which is able to predict the suitable therapeutic protocol. The objective of this graph is to help doctors classify breast cancer patients depending on the type of breast cancer and the appropriate therapeutic protocol and the optimal dose. In this article we focus on the use of the patient’s personal data and medical history for each patient, input features and the medical tests that patient already have done. The predictive machine learning model based on Neural Network, as well as on different input features and using other advanced Data mining algorithms.


Breast cancer Machine learning Neural network Therapeutic protocol Data mining Predictive model 


  1. 1.
    WHO: World Health OrganizationGoogle Scholar
  2. 2.
    Lalla Salma Foundation - Cancer Prevention and TreatmentGoogle Scholar
  3. 3.
    Elidrissi Errahhali, M., Elidrissi Errahhali, M., Ouarzane, M., Boulouiz, R., Bellaou, M.: Cancer incidence in eastern Morocco: cancer patterns and incidence trends. BMC Cancer 17, 587 (2017). (2005–2012)CrossRefGoogle Scholar
  4. 4.
    Rabat cancer registry (2005)Google Scholar
  5. 5.
    Parvez, A., Qamar, S., Rizvi, S.Q.A.: Techniques of data mining in healthcare: a review. Int. J. Comput. Appl. (0975 – 8887) 120(15), 38–50 (2015)Google Scholar
  6. 6.
    Apte, C., Weiss, S.M.: Data mining with decision trees and decision rules. T.J. Watson Research CenterGoogle Scholar
  7. 7.
    Podgorelec, V., Kokol, P., Stiglic, B., Rozman, I.: Decision trees: an overview and their use in medicine. J. Med. Syst. 26(5), 445–463 (2002)CrossRefGoogle Scholar
  8. 8.
    Sujatha, G., Usha Rani, K.: Evaluation of decision tree classifiers on tumor datasets. Int. J. Emerg. Trends Technol. Comput. Sci. (IJETTCS) (2013)Google Scholar
  9. 9.
    Gupta, S., Kumar, D., Sharma, A.: Data mining classification techniques applied for breast cancer diagnosis and prognosis (2011)Google Scholar
  10. 10.
    Chaurasia, V., Pal, S.: Data mining techniques: to predict and resolve breast cancer survivability. Int. J. Comput. Sci. Mob. Comput. 3(1), 10–22 (2014)Google Scholar
  11. 11.
    Xu, X., Zhang, Y., Zou, L., Wang, M., Li, A.: A gene signature for breast cancer prognosis using support vector machine. IEEE, 16–18 October 2012Google Scholar
  12. 12.
    Delen, D., Walker, G., Kadam, A.: Predicting breast cancer survivability: a comparison of three data mining methods. Artif. Intell. Med. (2004)Google Scholar
  13. 13.
    Asri, H., Mousannif, H., Al Moatassime, H., Noel, T.: Using machine learning algorithms for breast cancer risk prediction and diagnosis. In: The 6th International Symposium on Frontiers in Ambient and Mobile Systems (FAMS 2016) (2016)CrossRefGoogle Scholar
  14. 14.
    Shrivastava, S.S., Sant, A., Aharwal, R.P.: An overview on data mining approach on breast cancer data. Int. J. Adv. Comput. Res. 3(4), 256–262 (2013)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Sarah Khrouch
    • 1
    Email author
  • Mostafa Ezziyyani
    • 1
  • Mohammed Ezziyyani
    • 2
  1. 1.Faculty of Sciences and TechniquesAbdelmalek Essaâdi UniversityTangierMorocco
  2. 2.Polydisciplinary FacultyAbdelmalek Essaâdi UniversityLaracheMorocco

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