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Related Concept Videos

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Related Experiment Video

Updated: Sep 16, 2025

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
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Advancing breast cancer prediction using blockchain-secured hybrid genetic algorithm.

Monu Bhagat1, Ujjwal Maulik2

  • 1Department of Computer Science and Engineering, Birla Institute of Technology Mesra, Ranchi, Jharkhand, India; Department of Artificial Intelligence, Indian Institute of Technology Kharagpur, India.

Computers in Biology and Medicine
|July 4, 2025
PubMed
Summary

This study integrates evolutionary algorithms and blockchain with machine learning for breast cancer prediction. The combined approach achieved high accuracy, enhancing early diagnosis and protecting patient data privacy.

Keywords:
BlockchainBreast cancerFeature selectionGenetic algorithm (GA)Machine learningPrognosis

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Area of Science:

  • Medical Informatics
  • Computer Science
  • Biotechnology

Background:

  • Feature selection (FS) is crucial in machine learning for improving model performance and accuracy.
  • Breast cancer (BC) prediction can be enhanced using machine learning (ML) while ensuring data privacy and integrity through blockchain technology.

Purpose of the Study:

  • To explore feature selection using evolutionary algorithms (GA) for breast cancer prediction.
  • To develop and evaluate a secure system integrating blockchain with ML for breast cancer diagnosis.
  • To compare the performance of various ML algorithms and classifiers for breast cancer identification.

Main Methods:

  • Utilized the Wisconsin Diagnosis Breast Cancer dataset.
  • Implemented and compared multiple ML algorithms including XGBoost, AdaBoost, Logistic Regression, Linear SVM, Random Forest, KNN, Gradient Boosting, Radial SVM, and Decision Tree.
  • Integrated a blockchain method with smart contracts for secure data handling and prediction.

Main Results:

  • All tested ML algorithms achieved over 96.31% accuracy in distinguishing benign from malignant tumors.
  • The combination of Genetic Algorithms (GA) and Linear SVM yielded the highest accuracy at 99.47%.
  • XGBoost demonstrated superior performance overall, outperforming other algorithms with or without GA.

Conclusions:

  • Supervised machine learning techniques, particularly when combined with evolutionary algorithms and blockchain, show significant promise for early cancer diagnosis and prognosis.
  • The developed system offers a secure and impenetrable solution against data breaches and tampering in medical prediction processes.
  • Integrating blockchain with ML enhances the reliability and security of breast cancer prediction models.