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Related Experiment Video

Updated: Apr 6, 2026

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
07:41

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

9.7K

A distributed fusion framework for breast cancer recurrence prediction using MapReduce.

Prachi Shahare1,2, Abha Mahalwar2, Aniket K Shahade3

  • 1Department of Computer Science and Engineering, Yeshwantrao Chavan College of Engineering (YCCE), Nagpur, Maharashtra, India.

Scientific Reports
|April 4, 2026
PubMed
Summary

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Cancer Survival Analysis01:21

Cancer Survival Analysis

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Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
838

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This study introduces a hybrid AI framework for predicting breast cancer recurrence, combining multiple advanced machine learning models. The framework enhances prediction accuracy and scalability for diverse, large-scale datasets, improving clinical decision-making.

Area of Science:

  • Oncology
  • Artificial Intelligence
  • Data Science

Background:

  • Breast cancer recurrence poses a significant clinical challenge, impacting survival and treatment strategies.
  • Accurate early prediction is difficult due to data heterogeneity, imbalance, and distributed storage across institutions.

Purpose of the Study:

  • To develop a scalable, hybrid AI framework for accurate breast cancer recurrence prediction.
  • To address challenges of heterogeneous and distributed datasets in clinical settings.

Main Methods:

  • A MapReduce-aligned hybrid framework integrating Spark-based Gradient Boosted Trees, denoising autoencoder (AE) latent representations, calibrated XGBoost, and deep tabular models (FT-Transformer, TabTransformer).
  • Evaluation on SEER breast cancer recurrence and Wisconsin Diagnostic Breast Cancer datasets.
Keywords:
Big-data healthcare analyticsBreast cancer recurrenceFeature representation learningFusion ensemble learningImbalanced clinical dataMapReducePrognostic prediction

Related Experiment Videos

Last Updated: Apr 6, 2026

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
07:41

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

9.7K

Main Results:

  • The AE-augmented fusion framework and calibrated XGBoost achieved superior discrimination on the Wisconsin dataset (ROC-AUC 0.9954, MCC ≥ 0.981).
  • On the SEER dataset, the fusion framework improved recall for sparse recurrence signals, while calibrated XGBoost balanced precision and stability.
  • Fusion learning enhanced sensitivity and stability; calibrated XGBoost showed strong discrimination.

Conclusions:

  • The proposed framework offers a scalable and reliable solution for individualized breast cancer recurrence risk prediction.
  • Combining diverse AI techniques (tree-based, AE, transformers) improves prediction performance and robustness.
  • Spark-GBT integration ensures suitability for multi-institutional data without centralization.