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June 1, 2026Open Access

Extension of Breast Cancer Prediction Model using Random Forest

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Authors

RMRida Mohammed

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Overview

Randomized trial evaluates random forest model performance in breast cancer detection, suggesting improved accuracy.

Key Points

  • This research aims to enhance breast cancer prediction models using a Random Forest classifier.
  • Introduced a Random Forest classifier to the existing breast cancer prediction dataset.
  • Applied preprocessing techniques and a 75–25 train-test split.
  • Implemented hyperparameter tuning, balanced class weights, and 5-fold cross-validation.
  • The Random Forest classifier demonstrated improved accuracy over the Decision Tree classifier reported previously (91.92% vs. 87.12%).
  • Hyperparameter tuning and class balancing contributed to better model performance.

Cite This Study

Rida Mohammed (2026) studied this question.

synapsesocial.com/papers/6a1d234302fbce9130638d3bhttps://doi.org/10.5281/zenodo.20460407
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Development and Optimization of Random Forest Algorithm for Breast Cancer Diagnosis2024
  2. 2A Comparative Assessment of Machine Learning Algorithms for Detecting and Diagnosing Breast Cancer2024 · 6 citations
  3. 3Breast Cancer Classification Using Naïve Bayes and Random Forest Algorithms2025 · 1 citations
  4. 4A comparative study of decision tree and support vector machine for breast cancer prediction2024
  5. 5Feature Selection using Extra Trees for Breast Cancer Prediction2024 · 1 citations