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Supervised Machine Learning Predictive Analytics For Triple-Negative Breast Cancer Death Outcomes.

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Machine learning models can predict 5-year survival for triple-negative breast cancer patients. Key predictors include platelet count, lymphocyte-to-monocyte ratio, and age, aiding in personalized prognosis.

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Area of Science:

  • Oncology
  • Biostatistics
  • Machine Learning

Background:

  • Triple-negative breast cancer (TNBC) presents unique challenges in predicting patient outcomes.
  • Accurate prognostication is crucial for tailoring treatment and managing patient expectations.
  • Machine learning offers potential for developing predictive models in oncology.

Purpose of the Study:

  • To develop and evaluate machine learning algorithms for predicting the 5-year mortality risk in TNBC patients.
  • To identify key clinical and laboratory parameters influencing TNBC patient survival.
  • To provide a tool for estimating individual patient outcomes post-discharge.

Main Methods:

  • Analysis of 1570 stage I-III breast cancer patients treated at Sun Yat-sen Memorial Hospital.
  • Application of machine learning algorithms (including Random Forest, Gradient Boosting, Decision Tree, Logistic Regression) to predict 5-year death outcomes.
  • Evaluation of model performance using accuracy, Area Under the Curve (AUC), and Mean Squared Error (MSE).

Main Results:

  • Platelet count, lymphocyte-to-monocyte ratio (LMR), age, platelet-to-lymphocyte ratio (PLR), and white blood cell count were significant prognostic factors for TNBC.
  • Random Forest demonstrated high accuracy (0.770) and AUC (0.897) in the training group.
  • Performance varied across models and datasets, with Gradient Boosting and Decision Tree showing competitive results in the test group.

Conclusions:

  • Machine learning algorithms are effective in predicting 5-year mortality for TNBC patients.
  • The identified predictive factors can enhance the accuracy of individual outcome estimations.
  • This predictive capability can support clinical decision-making and patient counseling for TNBC.