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Prognostic Diagnosis for Breast Cancer Patients Using Probabilistic Bayesian Classification.

N Junath1, Alok Bharadwaj2, Sachin Tyagi3

  • 1The University of Technology and Applied Science Ibri Sultanate of Oman, Oman.

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Summary
This summary is machine-generated.

This study introduces a dynamic regression model using the lymph node ratio (LNR) to predict breast cancer survival. The Bayesian approach offers superior accuracy for patient prognosis and treatment decisions.

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Area of Science:

  • Oncology
  • Biostatistics
  • Data Science

Background:

  • Data analytics and machine learning are crucial for healthcare, aiding in diagnosis and treatment decisions.
  • Breast cancer categorization and prognosis evaluation present challenges due to large datasets.
  • Clinicopathological indicators significantly influence breast cancer patient outcomes.

Purpose of the Study:

  • To develop an efficient data categorization method for breast cancer.
  • To analyze the influence of clinicopathological indicators on prognosis and survival using the Bayesian method.
  • To build a dynamic regression model for prognosis analysis incorporating the lymph node ratio (LNR).

Main Methods:

  • Utilized the Bayesian method to analyze clinicopathological indicators and patient survival.
  • Employed logistic regression to estimate the overall lymph node ratio (LNR) in patients.
  • Developed a probabilistic Bayesian classifier-based dynamic regression model for prognosis.

Main Results:

  • The dynamic regression model using the total estimated LNR showed the best data fit.
  • This model achieved the highest overall survival forecast accuracy compared to other models.
  • The prognostic techniques provide patient-specific insights into nodal survival and status.

Conclusions:

  • The developed dynamic regression model accurately predicts breast cancer survival.
  • The framework offers a flexible approach applicable to various cancer types and datasets.
  • This method enhances prognostic understanding and supports clinical decision-making in oncology.