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Updated: Sep 2, 2025
07:41
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
9.1K
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.
Biomed Research International
|August 4, 2022
Summary
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.
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.
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