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Some Methods for Substantiating Diagnostic Decisions Made Using Machine Learning Algorithms.

A G Losev1, I E Popov1, A Yu Petrenko1

  • 1Volgograd State University, Volgograd, Russia.

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Summary
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This study evaluates classification algorithms for breast cancer diagnosis using microwave radiometry. A novel algorithm combining decision trees and a naive Bayesian classifier is presented for data-driven substantiation.

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Area of Science:

  • Biomedical Engineering
  • Medical Imaging
  • Machine Learning

Background:

  • Breast cancer diagnosis relies on accurate data interpretation.
  • Microwave radiometry offers a non-invasive method for detecting breast cancer.
  • Existing classification algorithms require evaluation for diagnostic accuracy.

Purpose of the Study:

  • To assess various classification algorithms for breast cancer diagnosis using microwave radiometry data.
  • To present a novel substantiation algorithm for diagnostic decision-making.
  • To demonstrate the utility of numerical data in supporting diagnoses.

Main Methods:

  • Review of principles of operation for different classification algorithms.
  • Development of a substantiation algorithm integrating decision trees and a naive Bayesian classifier.
  • Application of the algorithm to microwave radiometry data for breast cancer cases.

Main Results:

  • Evaluation of algorithm performance in classifying breast cancer based on microwave radiometry.
  • Demonstration of the proposed algorithm's ability to provide numerical data for diagnostic substantiation.
  • Successful application examples for breast cancer diagnosis.

Conclusions:

  • Classification algorithms are crucial for breast cancer diagnosis using microwave radiometry.
  • The presented decision tree and naive Bayesian classifier algorithm offers a robust method for diagnostic substantiation.
  • Numerical data derived from microwave radiometry can effectively support breast cancer diagnosis.