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Related Concept Videos

Cancer Survival Analysis01:21

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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...
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Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
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Related Experiment Video

Updated: Jul 15, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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Breast cancer prediction using different machine learning methods applying multi factors.

Elham Nazari1,2,3, Hamid Naderi1, Mahla Tabadkani4,2

  • 1Faculty of Medicine, Department of Medical Informatics, Mashhad University of Medical Sciences, Mashhad, Iran.

Journal of Cancer Research and Clinical Oncology
|September 29, 2023
PubMed
Summary

This study developed a highly accurate breast cancer risk prediction model using machine learning. The Random Forest technique achieved 99.3% accuracy by analyzing multifactorial features, improving early diagnosis potential.

Keywords:
Breast cancerCancer predictionFactor affectingMachine learning

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Area of Science:

  • Oncology and Computational Biology
  • Biostatistics and Bioinformatics

Background:

  • Breast cancer (BC) is a prevalent, multifactorial global disease.
  • Accurate risk prediction is crucial for early diagnosis and management.

Purpose of the Study:

  • To compare machine learning (ML) techniques for breast cancer risk prediction.
  • To develop a comprehensive BC risk model using diverse patient features.

Main Methods:

  • Utilized a dataset of 810 individuals (115 BC patients, 695 healthy).
  • Selected 45 key attributes from genetic, biochemical, biomarker, gender, demographic, and pathological factors.
  • Trained 13 ML models, evaluating attribute importance and internal relationships.

Main Results:

  • Random Forest (RF) demonstrated superior performance with 99.26% accuracy, 99% precision, and 99% AUC.
  • Pathology, biomarker, biochemistry, gene, and demographic factors significantly impacted BC risk (RF analysis).

Conclusions:

  • Identifying and quantifying risk factors enhances diagnostic accuracy.
  • The developed RF model, incorporating multifactorial features, achieved high accuracy for BC risk prediction.
  • This approach supports the development of comprehensive diagnostic tools.