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Updated: Jan 2, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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Automated Breast Cancer Diagnosis Based on Machine Learning Algorithms.

Habib Dhahri1,2, Eslam Al Maghayreh1,3, Awais Mahmood1

  • 1College of Applied Computer Sciences (ACS), Al-Muzahimiyah Branch, King Saud University, Riyadh, Saudi Arabia.

Journal of Healthcare Engineering
|December 10, 2019
PubMed
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This study uses genetic programming and machine learning to improve breast cancer diagnosis. The approach optimizes algorithms to accurately distinguish between benign and malignant tumors, enhancing diagnostic accuracy.

Area of Science:

  • Biomedical Engineering
  • Computational Biology
  • Medical Informatics

Background:

  • Machine learning and soft computing are increasingly used for breast cancer analysis.
  • Existing methods often claim superior speed, ease of use, or accuracy.
  • A need exists for optimized algorithms to improve tumor classification accuracy.

Purpose of the Study:

  • To develop a system using genetic programming and machine learning to accurately differentiate benign and malignant breast tumors.
  • To optimize machine learning classifiers by selecting optimal features and parameters.
  • To enhance the performance of breast cancer diagnostic models.

Main Methods:

  • Applied genetic programming for feature selection and parameter optimization.
  • Utilized machine learning classifiers for tumor differentiation.

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  • Evaluated performance using sensitivity, specificity, precision, accuracy, and ROC curves.

    • Main Results:

    • Genetic programming effectively identified optimal features and classifier parameters.
    • The proposed method demonstrated high performance in distinguishing between tumor types.
    • The study confirmed the capability of genetic programming in model optimization.

    Conclusions:

    • Genetic programming can automatically discover optimal models for breast cancer diagnosis.
    • Combining feature preprocessing with classifier algorithms via genetic programming enhances diagnostic accuracy.
    • This approach offers a robust method for improving breast tumor classification.