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Homogeneous Adaboost Ensemble Machine Learning Algorithms with Reduced Entropy on Balanced Data.

Mahesh Thyluru Ramakrishna1, Vinoth Kumar Venkatesan2, Ivan Izonin3

  • 1Department of Computer Science and Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Bangalore 562112, India.

Entropy (Basel, Switzerland)
|February 25, 2023
PubMed
Summary
This summary is machine-generated.

This study predicts breast cancer (BC) using Adaboost ensemble methods, achieving 97.95% accuracy. The approach combines decision trees and naive Bayes with synthetic minority over-sampling technique for improved classification of this public health concern.

Keywords:
breast cancerensemble methodsentropymachine learningprecision

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Area of Science:

  • Oncology and Public Health
  • Machine Learning and Data Science

Background:

  • Breast cancer (BC) is a significant global public health issue, disproportionately affecting women and often diagnosed at advanced stages.
  • The increasing prevalence of BC in both developed and developing nations necessitates advanced diagnostic and predictive tools.

Purpose of the Study:

  • To predict and classify breast cancer (BC) using Adaboost ensemble techniques.
  • To leverage ensemble methods to manage individual model strengths and weaknesses for optimal prediction accuracy.

Main Methods:

  • Utilized Adaboost ensemble techniques, integrating decision trees (DT) and naive Bayes (NB) classifiers.
  • Employed synthetic minority over-sampling technique (SMOTE) for data pre-processing to address class imbalance and noise.
  • Computed weighted entropy for target column classification, with weights representing class likelihood.

Main Results:

  • Achieved a high prediction accuracy of 97.95% using the Adaboost-random forest classifier.
  • Demonstrated the effectiveness of ensemble methods in improving breast cancer classification accuracy.

Conclusions:

  • Adaboost ensemble techniques, particularly with random forest integration, offer a robust approach for accurate breast cancer prediction.
  • The study highlights the potential of data mining pre-processing techniques like SMOTE in enhancing predictive model performance for imbalanced datasets.