PulseTrendingJournal ClubResearchersJournalsExplore
Instagram
HomeTrendingJournal ClubExplore
Synapse
⌘+K
Synapse
April 21, 2026Open Access

Development and Optimization of Random Forest Algorithm for Breast Cancer Diagnosis

View Full Paper
Ask AI
Bookmark
Share

Authors

IWIbrahim WaziriUMUmar Nasir MuhtarAAAdam Najib Adam

Discussion

Loading...

Member takes

Overview

Optimizes the Random Forest algorithm for high accuracy in breast cancer classification, suggesting significant advancements in early detection.

Key Points

  • The aim is to develop an optimized Random Forest algorithm for accurately diagnosing breast cancer.
  • Utilized grid search to fine-tune random forest hyperparameters.
  • Set hyperparameters for the model including trees, depth, and samples for splitting and leaves.
  • Trained the model on breast cancer data using the optimized parameters.
  • Achieved 99.12% accuracy in classifying breast cancer as benign or malignant.
  • Surpassed previous results and established algorithms in terms of accuracy.
  • Demonstrated robust performance in identifying patterns within breast cancer datasets.

Cite This Study

Waziri et al. (2024) studied this question.

synapsesocial.com/papers/69e7143fcb99343efc98da6ehttps://doi.org/10.5281/zenodo.19648800
View Full Paper
Ask AI
Bookmark
Share

Also Consider

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

  1. 1A Comparative Assessment of Machine Learning Algorithms for Detecting and Diagnosing Breast Cancer2024 · 8 citations
  2. 2Breast Cancer Classification Using Naïve Bayes and Random Forest Algorithms2025 · 1 citations
  3. 3Extension of Breast Cancer Prediction Model using Random Forest2026
  4. 4Optimizing Breast Cancer Diagnosis with Machine Learning Algorithms2024 · 1 citations
  5. 5An Efficient Breast Cancer Detection Using Machine Learning Classification Models2024 · 8 citations