IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v424y2022ics0096300322001242.html
   My bibliography  Save this article

A hybrid high‐order type‐2 FCM improved random forest classification method for breast cancer risk assessment

Author

Listed:
  • Hosseinpour, Mahsa
  • Ghaemi, Sehraneh
  • Khanmohammadi, Sohrab
  • Daneshvar, Sabalan

Abstract

One of the main challenges in breast cancer risk assessment is to provide a patient with an easily interpretable perspective on her disease's situation. This paper proposes a new method for accessing breast cancer risk, called Hybrid High-order Type-2 Fuzzy Cognitive Map Improved Random Forest Classification (H-HT2 FCM IRFC, in which, by taking an exact analysis, the disease risk is qualitatively obtained in three modes, i.e., each optimistic, realistic and pessimistic. Using either FCM or high-order FCM does not make a favorable response in uncertain situations where applying type-2 fuzzy to obtain the weights of FCM will have much better answers. A hybrid version of high-order type-2 FCM, proposed in this work, enables us to assess the breast cancer risk in three modes of optimistic, realistic, and pessimistic. The proposed method has three levels; at the first level, patient profile, family history, and inherited factors are tested by high-order FCM. At the second level, by examining the mass characteristics obtained from the mammograms, the disease risk is achieved by hybrid high-order type-2 FCM in three modes of optimistic, realistic, and pessimistic. The position of the tumor's effect on breast cancer is obtained in the third level by the fuzzy method. Finally, an overall breast cancer risk is predicted by a new algorithm, called Improved Random Forest Classification, which results in superior performance. Compared with the existing methods, the accuracy of the results obtained from the proposed method is desirable. The three-mode assessment will help the patients and their physician (oncologist) run the best treatment. Finally, the proposed method is successfully tested on an actual medical dataset.

Suggested Citation

  • Hosseinpour, Mahsa & Ghaemi, Sehraneh & Khanmohammadi, Sohrab & Daneshvar, Sabalan, 2022. "A hybrid high‐order type‐2 FCM improved random forest classification method for breast cancer risk assessment," Applied Mathematics and Computation, Elsevier, vol. 424(C).
    • Handle: RePEc:eee:apmaco:v:424:y:2022:i:c:s0096300322001242
    DOI: 10.1016/j.amc.2022.127038
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300322001242
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.amc.2022.127038?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Min-Wei Huang & Chih-Wen Chen & Wei-Chao Lin & Shih-Wen Ke & Chih-Fong Tsai, 2017. "SVM and SVM Ensembles in Breast Cancer Prediction," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-14, January.
    2. Amirkhani, Abdollah & Papageorgiou, Elpiniki I. & Mosavi, Mohammad R. & Mohammadi, Karim, 2018. "A novel medical decision support system based on fuzzy cognitive maps enhanced by intuitive and learning capabilities for modeling uncertainty," Applied Mathematics and Computation, Elsevier, vol. 337(C), pages 562-582.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Meshwa Rameshbhai Savalia & Jaiprakash Vinodkumar Verma, 2023. "Classifying Malignant and Benign Tumors of Breast Cancer: A Comparative Investigation Using Machine Learning Techniques," International Journal of Reliable and Quality E-Healthcare (IJRQEH), IGI Global, vol. 12(1), pages 1-19, January.
    2. Rongjun Chen & Jinhui Lin, 2020. "Identification of feature risk pathways of smoking-induced lung cancer based on SVM," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-16, June.
    3. Liang Song & Shanjun Liu & Wenwen Li, 2019. "Quantitative Inversion of Fixed Carbon Content in Coal Gangue by Thermal Infrared Spectral Data," Energies, MDPI, vol. 12(9), pages 1-17, May.
    4. Ghaboulian Zare, Sara & Alipour, Mohammad & Hafezi, Mehdi & Stewart, Rodney A. & Rahman, Anisur, 2022. "Examining wind energy deployment pathways in complex macro-economic and political settings using a fuzzy cognitive map-based method," Energy, Elsevier, vol. 238(PA).
    5. Chen, Chen-Tung & Chiu, Yen-Ting, 2021. "A study of dynamic fuzzy cognitive map model with group consensus based on linguistic variables," Technological Forecasting and Social Change, Elsevier, vol. 171(C).
    6. Cheong Kim & Francis Joseph Costello & Kun Chang Lee, 2019. "Integrating Qualitative Comparative Analysis and Support Vector Machine Methods to Reduce Passengers’ Resistance to Biometric E-Gates for Sustainable Airport Operations," Sustainability, MDPI, vol. 11(19), pages 1-22, September.
    7. Themistoklis Koutsellis & Georgios Xexakis & Konstantinos Koasidis & Alexandros Nikas & Haris Doukas, 2022. "Parameter analysis for sigmoid and hyperbolic transfer functions of fuzzy cognitive maps," Operational Research, Springer, vol. 22(5), pages 5733-5763, November.
    8. Linmao Ma & Jing Yu & Long Zhang, 2019. "An Analysis on Barriers to Biomass and Bioenergy Development in Rural China Using Intuitionistic Fuzzy Cognitive Map," Energies, MDPI, vol. 12(9), pages 1-23, April.
    9. Maryam Mahsal Khan & Alexandre Mendes & Stephan K Chalup, 2018. "Evolutionary Wavelet Neural Network ensembles for breast cancer and Parkinson’s disease prediction," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-15, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:apmaco:v:424:y:2022:i:c:s0096300322001242. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/applied-mathematics-and-computation .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.