An amalgamated prediction model for breast cancer detection using fuzzy features

Smita Jhajharia, Seema Verma, Rajesh Kumar

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

Input feature processing is required for obtaining meaningful results for cancer prognosis. In this paper, the extended Kalman filter (EKF) and fuzzy K-means clustering algorithms have been combined into a hybrid algorithm with improved functionality, compared to either of the two separately. The proposed hybrid algorithm implements fuzzy K-means with support vector machine (SVM) coupled with an EKF for data filtering, working with from consecutive filtering and prediction cycles. Fuzzy membership functions are then calculated to map the labels with the attributes which is used by K-means to create a new modified set of attributes supplied to the SVM classifier, with lesser number of support vectors. The number of clusters is added into the training process as the input parameter except the kernel parameters and the SVM penalty factor. The approach was tested for various publicly available datasets like UCL, SEER and a real dataset compiled by the authors.

Original languageEnglish
Pages (from-to)345-356
Number of pages12
JournalInternational Journal of Medical Engineering and Informatics
Volume12
Issue number4
Publication statusPublished - 2020
Externally publishedYes

Keywords

  • Cancer
  • Clustering
  • EKF
  • Extended Kalman filter
  • Fuzzy K-means

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Biomaterials
  • Biomedical Engineering
  • Health Informatics

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