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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Optimized Attribute Selection Using Artificial Plant (AP) Algorithm with ESVM Classifier (AP-ESVM) and Improved

V Saravanan1, R Manikandan2, K S Maharasan3

  • 1Dr. SNS Rajalakshmi College of Arts and Science, Coimbatore, India. tvsaran@hotmail.com.

Interdisciplinary Sciences, Computational Life Sciences
|June 14, 2020
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Summary
This summary is machine-generated.

Feature selection in big data bioinformatics is crucial for accurate disease identification. This study introduces a novel approach using Artificial Plant algorithm and Enhanced Support Vector Machine for effective feature selection in large medical datasets.

Keywords:
Artificial plant (AP)BAT optimization and big dataClinicalDatabasesFeature selection (FS)Singular value decomposition (SVD)

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Area of Science:

  • Bioinformatics and Computational Biology
  • Artificial Intelligence in Medicine
  • Data Mining and Machine Learning

Background:

  • The era of Big Data in bioinformatics presents challenges in managing and analyzing large volumes of genetic and clinical information.
  • High dimensionality in Artificial Intelligence (AI) issues necessitates effective feature selection (FS) to reduce complexity and improve model performance.
  • Inaccurate or irrelevant disease identifiers in large clinical databases can negatively impact disease identification accuracy and physician decision support systems.

Purpose of the Study:

  • To present a novel feature selection approach for high-dimensional medical data.
  • To enhance the accuracy and efficiency of disease identification in large clinical datasets.
  • To reduce the learning time for AI systems used in medical decision support.

Main Methods:

  • Feature selection using the Artificial Plant algorithm coupled with an Enhanced Support Vector Machine classifier.
  • Dimensionality reduction of selected features via an Improved Singular Value Decomposition strategy.
  • Optimization of the feature selection process using the BAT optimization technique.

Main Results:

  • The proposed method demonstrated superior effectiveness compared to existing approaches on real-time large cervical cancer data.
  • Successful identification and removal of irrelevant disease identifiers, leading to improved data quality.
  • Enhanced accuracy and efficiency in the classification of cervical cancer.

Conclusions:

  • The developed feature selection technique offers a robust solution for handling Big Data challenges in medical bioinformatics.
  • The integration of Artificial Plant algorithm, Enhanced Support Vector Machine, Improved Singular Value Decomposition, and BAT optimization yields significant performance gains.
  • This approach has the potential to improve diagnostic accuracy and support clinical decision-making in various medical applications.