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January 1, 2025European Journal of Scientific Research and Reviews.

Early Detection and High-Risk Feature Identification in Breast Cancer: A Two-Stage Machine Learning Approach

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Authors

PAPrincewill AkpojotorOOOluwafemi Alabi OkunlolaRFRantiola Famutimi

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Overview

Two-stage machine learning approach improves breast cancer classification, suggesting enhanced early detection methods.

Key Points

  • High accuracy of 97% was achieved in breast cancer prediction, indicating the model's effectiveness.
  • Feature importance analysis highlighted that radius_se was the strongest predictor of malignancy outcomes.
  • Random Forest and SVM-RFE were utilized for feature extraction, emphasizing an innovative approach to diagnostics.
  • Results support potential clinical applications in improving early diagnosis and risk stratification for patients.

Cite This Study

Akpojotor et al. (2025) studied this question.

synapsesocial.com/papers/68af6203ad7bf08b1eae2c24https://doi.org/10.5455/ejsrr.20250428075216
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Also Consider

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  1. 1Importance of Data Preprocessing for Accurate and Effective Prediction of Breast Cancer: Evaluation of Model Performance in Novel Data2025
  2. 2Performance Analysis of Breast Cancer Classification Using Feature Selection and Machine Learning2024
  3. 3Development of a machine learning predictive model for early detection of breast cancer2026
  4. 4Predictive Model Using Machine Leaning Approach for the Detection of Breast Cancer2024 · 1 citations
  5. 5Applying machine learning to breast cancer diagnosis: A high school student’s exploration using R2025