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Breast cancer prediction using a hybrid method based on Butterfly Optimization Algorithm and Ant Lion Optimizer.

Shankar Thawkar1, Satish Sharma2, Munish Khanna3

  • 1Department of Information Technology, Hindustan College of Science and Technology, Mathura, Uttar Pradesh, India.

Computers in Biology and Medicine
|November 4, 2021
PubMed
Summary

A new hybrid Butterfly Optimization Algorithm-Ant Lion Optimizer (BOAALO) method enhances breast cancer detection by selecting optimal features. This approach improves diagnostic accuracy and efficiency in identifying malignant or benign breast tissue from mammograms.

Keywords:
Adaptive neuro-fuzzy inference systemAnt lion optimizerArtificial neural networkBreast cancerButterfly optimization algorithmFeature selectionMammographySupport vector machine

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Area of Science:

  • Biomedical Engineering
  • Computational Biology
  • Medical Imaging

Background:

  • Accurate breast cancer detection relies on effective feature selection for computer-based systems.
  • Reducing feature dimensionality is crucial for improving diagnostic model performance.

Purpose of the Study:

  • To introduce a hybrid Butterfly Optimization Algorithm-Ant Lion Optimizer (BOAALO) for feature selection in breast cancer detection.
  • To evaluate the efficacy of BOAALO in improving the accuracy of breast cancer diagnosis using machine learning classifiers.

Main Methods:

  • A hybrid BOAALO feature selection technique was developed.
  • Selected features were used with artificial neural network, adaptive neuro-fuzzy inference system, and support vector machine classifiers.
  • The method was validated on 651 mammogram images and a benchmark dataset.

Main Results:

  • BOAALO demonstrated superior performance over individual BOA and ALO algorithms.
  • The hybrid method achieved higher accuracy, sensitivity, specificity, and better ROC curve performance.
  • BOAALO identified an optimal feature subset, enhancing diagnostic precision.

Conclusions:

  • The BOAALO method offers a robust and accurate approach for breast cancer diagnosis.
  • This technique effectively reduces feature dimensionality, leading to improved classification performance.
  • The findings support the clinical applicability of BOAALO for automated breast cancer detection.