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October 23, 2025Big Data and Cognitive ComputingOpen Access

Importance of Data Preprocessing for Accurate and Effective Prediction of Breast Cancer: Evaluation of Model Performance in Novel Data

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Authors

VBVekani BaloyiJMJamolbek MattievSMSello Mokwena

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Overview

Evaluation reveals that data preprocessing enhances model performance in breast cancer prediction, supporting machine learning applications.

Key Points

  • Prediction accuracy improves through effective data preprocessing and feature selection for breast cancer.
  • Logistic regression achieves notable model performance alongside dimensionality reduction methods applied.
  • Assessment evaluated multiple machine learning classifiers on a novel dataset for optimized prediction.
  • Statistical significance testing highlights improved performance of complex models over simpler alternatives.

Cite This Study

Baloyi et al. (2025) studied this question.

synapsesocial.com/papers/68f9d6583f3788722249277dhttps://doi.org/10.3390/bdcc9100266
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Also Consider

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

  1. 1Diagnosing Breast Cancer Using AI: A Comparison of Deep Learning and Traditional Machine Learning Methods2024 · 2 citations
  2. 2Development of a machine learning predictive model for early detection of breast cancer2026
  3. 3Performance Analysis of Breast Cancer Classification Using Feature Selection and Machine Learning2024
  4. 4Early Detection and High-Risk Feature Identification in Breast Cancer: A Two-Stage Machine Learning Approach2025
  5. 5Transforming Breast Cancer Prediction: Advanced Machine Learning Models for Accurate Prediction and Personalized Care2025