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Related Experiment Video

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Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
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Decision Support System for Breast Cancer Detection Using Biomarker Indicators.

Spiridon Vergis1, Konstantinos Bezas1, Themis P Exarchos2

  • 1Ionian University, Department of Informatics, Corfu, Greece.

Advances in Experimental Medicine and Biology
|January 1, 2022
PubMed
Summary

Machine learning aids early breast cancer detection using accessible data like age and glucose levels. Gradient Boosting Classification achieved the highest prediction accuracy, enabling a user-friendly mobile app for rapid risk assessment.

Keywords:
BiomarkersBreast cancer detectionCancer detectionDecision support systemsMachine learning

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Area of Science:

  • Oncology
  • Biomedical Engineering
  • Data Science

Background:

  • Breast cancer is a leading cancer in women, with early detection crucial for reducing mortality.
  • Traditional diagnostic tools like imaging may lack accessibility.
  • Blood-based biomarkers and machine learning offer promising alternatives for accessible early detection.

Purpose of the Study:

  • To develop a machine learning-based decision support system for early breast cancer detection.
  • To utilize easily obtainable user information, including age, BMI, glucose, and resistin levels.
  • To create an accessible mobile application for rapid breast cancer risk prediction.

Main Methods:

  • Exploration of machine learning algorithms: Logistic Regression, Naive Bayes, Support Vector Machine, and Gradient Boosting Classification.
  • Training and classification of new patients using a dataset of previous breast cancer incidents.
  • Development of a mobile application for user data input and prediction output.

Main Results:

  • Gradient Boosting Classification emerged as the optimal algorithm, demonstrating the highest prediction scores.
  • The developed system effectively classifies patients based on the selected input features.
  • The mobile application provides rapid results for user-inputted information.

Conclusions:

  • Machine learning, particularly Gradient Boosting Classification, can effectively support early breast cancer detection using accessible data.
  • The developed mobile application enhances accessibility for breast cancer risk assessment.
  • This approach offers a scalable and potentially cost-effective method for widespread screening.