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Enhancing Breast Cancer Detection and Classification Using Advanced Multi-Model Features and Ensemble Machine

Mana Saleh Al Reshan1, Samina Amin2, Muhammad Ali Zeb2

  • 1Department of Information Systems, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia.

Life (Basel, Switzerland)
|October 28, 2023
PubMed
Summary
This summary is machine-generated.

This study developed an advanced ensemble machine learning model for breast cancer detection. The model achieved 99.89% accuracy, offering a reliable system for diagnosing malignant tumors.

Keywords:
Wisconsin Diagnostic Breast Cancerbreast cancerclassificationdetectionensemble learningfeature selectionmachine learning

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Area of Science:

  • Oncology
  • Biomedical Engineering
  • Data Science

Background:

  • Breast cancer (BC) is the most prevalent cancer in women, necessitating accurate diagnostic tools.
  • Early detection of BC is crucial for effective treatment planning.
  • Current methods like fine needle aspiration (FNA) cytology and machine learning (ML) aid in diagnosis.

Purpose of the Study:

  • To develop an automated system for detecting and classifying breast tumors as benign or malignant.
  • To identify the minimal set of features for optimal breast cancer detection accuracy.
  • To enhance clinical diagnostic capabilities through advanced computational methods.

Main Methods:

  • Utilized the Wisconsin Diagnostic Breast Cancer (WDBC) dataset for classification.
  • Employed ensemble machine learning (EML) techniques, including voting, bagging, stacking, and boosting.
  • Implemented recursive feature elimination for feature selection to identify key diagnostic indicators.

Main Results:

  • The proposed EML model achieved high performance across six evaluation metrics.
  • The stacking model demonstrated superior performance with an average accuracy of 99.89%.
  • Achieved sensitivity, specificity, F1-score, precision, and AUC/ROC of 1.00%, 0.999%, 1.00%, 1.00%, and 1.00%, respectively.

Conclusions:

  • The developed EML approach provides a reliable and highly accurate system for breast cancer diagnosis.
  • Findings support the creation of dependable clinical detection systems for improved decision-making.
  • The proposed methodology shows potential for application in detecting other cancer types.