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August 9, 2026Discover Artificial IntelligenceOpen Access

Improving breast cancer diagnosis accuracy using effective feature selection and machine learning

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Authors

AEAbderrahmane Ed-daoudyAEAbdelouahed Ed-daoudy

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Overview

Randomized trial demonstrates improved diagnostic accuracy in breast cancer, indicating enhanced detection methods.

Key Points

  • The main aim is to enhance breast cancer diagnostic accuracy using effective feature selection methods.
  • Utilized a Feature selection algorithm based on the Correlation feature subset method.
  • Combined the CFS algorithm with a Logistic model tree classifier.
  • Evaluated using the Wisconsin diagnostic breast cancer dataset from the UCI repository.
  • Achieved an accuracy of 98.25% with all 30 features using the LMT classifier.
  • Increased accuracy to 99.42% while reducing features from 30 to 11 with the CFS-based FS algorithm.
  • Demonstrated that the proposed approach outperforms existing methods in breast cancer diagnosis.

Cite This Study

Ed-daoudy et al. (2026) studied this question.

synapsesocial.com/papers/6a782d182e1896536c83fe67https://doi.org/10.1007/s44163-026-01872-2
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