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Hypergraph Based Feature Selection Technique for Medical Diagnosis.

Nivethitha Somu1, M R Gauthama Raman1, Kannan Kirthivasan2

  • 1Centre for Information Super Highway (CISH), School of Computing, SASTRA University, Thanjavur, Tamil Nadu, India.

Journal of Medical Systems
|September 26, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a new feature selection method, Rough Set based K-Helly (RSKHT), for high-dimensional data. RSKHT efficiently identifies optimal feature subsets, improving machine learning model performance in medical diagnostics.

Keywords:
Feature selectionHigh dimensional datasetsHypergraphK – Helly propertyMedical diagnosisRough set theory (RST)

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Area of Science:

  • Data Science
  • Machine Learning
  • Computational Biology

Background:

  • High-dimensional datasets from information systems pose computational challenges.
  • Feature selection is crucial for efficient machine learning model development.
  • Existing methods may struggle with large datasets and complex feature interactions.

Purpose of the Study:

  • To develop a novel feature selection technique for optimal reduct identification.
  • To enhance machine learning model robustness and efficiency in medical diagnostics.
  • To address computational costs associated with high-dimensional data.

Main Methods:

  • Hybridization of Rough Set Theory (RST) and K-Helly property of hypergraph representation.
  • Development of a Rough Set based K-Helly feature selection technique (RSKHT).
  • Validation using medical datasets from the UCI repository and WEKA tool.

Main Results:

  • RSKHT demonstrates superiority over existing feature selection techniques.
  • Achieved significant improvements in reduct size, classification accuracy, and time complexity.
  • Validated computational attractiveness and flexibility for massive datasets.

Conclusions:

  • RSKHT offers an effective approach for feature selection in high-dimensional data.
  • The technique shows strong potential for medical diagnostic applications.
  • RSKHT provides a computationally efficient and flexible solution for big data challenges.