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Breast cancer disease classification using fuzzy-ID3 algorithm with FUZZYDBD method: automatic fuzzy database

Nur Farahaina Idris1, Mohd Arfian Ismail1

  • 1Faculty of Computing, College of Computing and Applied Sciences, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia.

Peerj. Computer Science
|May 20, 2021
PubMed
Summary

This study introduces the fuzzy-ID3 (FID3) algorithm for improved breast cancer detection. FID3 enhances the ID3 algorithm, offering higher accuracy in classifying continuous-valued data for early breast cancer diagnosis.

Keywords:
Breast cancerClassificationFID3 algorithmFUZZYDBDFuzzificationFuzzyFuzzy decision treeID3 algorithm

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Area of Science:

  • Oncology
  • Computer Science
  • Artificial Intelligence

Background:

  • Breast cancer is a leading cause of death in women globally, with early detection crucial for survival.
  • Current diagnostic methods face challenges in accurately and rapidly classifying breast cancer due to the complex, multistep nature of cell growth.
  • Existing algorithms like ID3 struggle with continuous-valued data, limiting their effectiveness in breast cancer detection.

Purpose of the Study:

  • To propose the fuzzy-ID3 (FID3) algorithm, a fuzzy decision tree, for enhanced breast cancer classification.
  • To address the limitations of the ID3 algorithm, specifically its inability to handle continuous-valued data and to improve classification accuracy.
  • To integrate fuzzy logic with decision tree techniques for more robust breast cancer diagnosis.

Main Methods:

  • Developed the fuzzy-ID3 (FID3) algorithm, combining fuzzy systems with the ID3 decision tree learning algorithm.
  • Utilized the FUZZYDBD method for automatic fuzzy database definition and data fuzzification within the FID3 framework.
  • Applied the fuzzified dataset to the FID3 algorithm for rule extraction and classification of new instances.

Main Results:

  • The FID3 algorithm demonstrated reliable and robust performance in breast cancer classification across three datasets (WBCD, WDBC, Coimbra).
  • The proposed method showed improved classification accuracy compared to existing methods, particularly in handling continuous-valued data.
  • Direct rule extraction from the FID3 decision tree enabled simple inference for classifying new patient data.

Conclusions:

  • The combination of the FID3 algorithm and the FUZZYDBD method provides an effective approach for breast cancer classification.
  • FID3 offers a significant advancement over traditional ID3 by successfully managing continuous data and enhancing diagnostic accuracy.
  • This fuzzy decision tree approach shows promise for accurate and efficient early detection of breast cancer, potentially improving patient outcomes.