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Related Experiment Video

Updated: Sep 14, 2025

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Detection of breast cancer using machine learning and explainable artificial intelligence.

Tharunya Arravalli1, Krishnaraj Chadaga2, H Muralikrishna3

  • 1Department of Electronics and Communication Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.

Scientific Reports
|July 24, 2025
PubMed
Summary

Machine learning models accurately identified breast cancer using patient data. Explainable AI revealed key diagnostic factors, with Random Forest achieving an 84% F1-score for improved clinical decision-making.

Keywords:
AI in healthcareAttribute selectionBreast cancerDiagnosisEnsemble classifierExplainable artificial intelligenceMachine learning

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Area of Science:

  • Oncology
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Breast cancer is a significant global health concern, characterized by uncontrolled proliferation of malignant cells.
  • Rising incidence necessitates advancements in early detection and accurate diagnosis.
  • Environmental and genetic factors contribute to breast cancer development.

Purpose of the Study:

  • To utilize machine learning classifiers for accurate breast cancer identification.
  • To incorporate explainable artificial intelligence (XAI) for model interpretability.
  • To identify key diagnostic features influencing breast cancer prediction.

Main Methods:

  • Employed various machine learning classifiers on patient diagnostic characteristics.
  • Integrated XAI techniques including SHAP, LIME, ELI5, Anchor, and QLattice.
  • Evaluated model performance using metrics such as the F1-score.

Main Results:

  • Random Forest classifier achieved the highest performance with an F1-score of 84%.
  • A stacked ensemble model demonstrated strong performance with an 83% F1-score.
  • XAI methods provided insights into the factors driving model predictions.

Conclusions:

  • Machine learning, enhanced by XAI, shows significant potential in assisting breast cancer diagnosis.
  • Interpretable models can reduce diagnostic errors and support clinical decision-making.
  • This approach offers a pathway to more transparent and reliable cancer detection.