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Updated: Sep 6, 2025

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
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Comparative analysis of classification algorithms on the breast cancer recurrence using machine learning.

Valentina Mikhailova1, Gholamreza Anbarjafari2,3,4

  • 1Institute of Technology, University of Tartu, Tartu, Estonia.

Medical & Biological Engineering & Computing
|July 5, 2022
PubMed
Summary

This study compared machine learning algorithms for breast cancer recurrence prediction. Support Vector Machine (SVM) achieved the highest accuracy and sensitivity, identifying tumor malignancy as a key predictor.

Keywords:
Breast cancerJ48Machine learningMedical imagingMultilayer Perceptron

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Area of Science:

  • Biomedical Informatics
  • Machine Learning
  • Computational Biology

Background:

  • Accurate prediction of breast cancer recurrence is crucial for effective patient management.
  • Various machine learning algorithms offer potential for predictive modeling in oncology.
  • Comparative analysis is needed to identify optimal algorithms for this clinical challenge.

Purpose of the Study:

  • To comparatively evaluate multiple classification algorithms for predicting breast cancer recurrence.
  • To determine the most effective predictive modeling technique for breast cancer recurrence classification.
  • To assess the contribution of different attributes to the classification models.

Main Methods:

  • Utilized Waikato Environment for Knowledge Analysis (WEKA) software for comparative evaluation.
  • Compared Naïve Bayes, J48, K*, Random Forest, Multilayer Perceptron (MLP), and Support Vector Machine (SVM) models.
  • Evaluated model performance using accuracy, precision, sensitivity, specificity, mean absolute error, ROC curves, and AUC values.

Main Results:

  • Support Vector Machine (SVM) achieved the highest accuracy (79.6%) and sensitivity (99%).
  • Multilayer Perceptron (MLP) demonstrated the highest precision (79%).
  • Tumor malignancy degree was identified as the most significant attribute influencing breast cancer recurrence.

Conclusions:

  • Support Vector Machine (SVM) is a highly effective algorithm for breast cancer recurrence prediction.
  • The degree of malignant tumor is a critical factor in predicting breast cancer recurrence.
  • This comparative study provides valuable insights for developing robust breast cancer predictive models.