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A machine learning model for assessing the development of breast cancer based on medical history and other indicators, which do not require special medical measuring devices, has been presented. Various machine learning models have been compared. Models based on decision trees and random forests have proven to be the most effective. The result was a random forest model that correctly classifies 95% of healthy individuals and 69% of the at-risk group. The generalization ability of the model was tested on a different dataset.

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Detection of Breast Cancer Risk Using Machine Learning Methods Based on Features Selected According to Diagnostic Models

  • 章节

  • 发表日期：

    2026年

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  • Kirill Dyomin,
  • Ilya Germashev,
  • Ibrahima Niang

摘要

A machine learning model for assessing the development of breast cancer based on medical history and other indicators, which do not require special medical measuring devices, has been presented. Various machine learning models have been compared. Models based on decision trees and random forests have proven to be the most effective. The result was a random forest model that correctly classifies 95% of healthy individuals and 69% of the at-risk group. The generalization ability of the model was tested on a different dataset.