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Related Experiment Video

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Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
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Automatic Classification on Bio Medical Prognosisof Invasive Breast Cancer

Sountharrajan S1, Karthiga M, Suganya E

  • 1Department of Computer Science and Engineering, Bannari Amman Institute of Technology, India.Email: rajancsg@gmail.com

Asian Pacific Journal of Cancer Prevention : APJCP
|September 28, 2017
PubMed
Summary

This study integrates biosensor data with data mining for breast cancer detection. The system achieved 79.25% accuracy using a Support Vector Machine classifier, improving early diagnosis and patient survival rates.

Keywords:
Support vector machinereceiver operating curvesurface acoustic wavehuman epidermal growth receptor

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Area of Science:

  • Biomedical Engineering
  • Data Science
  • Oncology

Background:

  • Breast cancer is a leading cause of death in women worldwide.
  • Early detection and prevention are crucial for reducing mortality.
  • Biomarker detection and data analysis are key to improving diagnostic accuracy.

Purpose of the Study:

  • To develop a novel system for breast cancer detection using biosensor technology and data mining.
  • To accurately predict disease development proportion and improve diagnostic accuracy.
  • To enhance patient survivability through improved drug design based on accurate prognosis.

Main Methods:

  • Utilized Surface Acoustic Waves (SAW) biosensor for label-free detection of the HER-2/neu cancer biomarker.
  • Integrated real-time biosensor data with the Wisconsin dataset for comprehensive analysis.
  • Applied various data mining classification algorithms, including Support Vector Machine (SVM).
  • Employed the Ranker algorithm for attribute ranking to enhance model precision.

Main Results:

  • Achieved a high accuracy of 79.25% using the SVM classifier.
  • Obtained an Area Under the Curve (ROC) of 0.754, outperforming existing systems.
  • Demonstrated the effectiveness of the integrated biosensor and data mining approach for breast cancer prognosis.

Conclusions:

  • The proposed system offers a promising approach for early breast cancer detection and prognosis.
  • The integration of SAW biosensor technology and data mining significantly improves diagnostic accuracy.
  • The findings can aid in designing targeted therapies and improving patient outcomes.