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Enhancement of Classifier Performance with Adam and RanAdam Hyper-Parameter Tuning for Lung Cancer Detection from

Karthika M S1, Harikumar Rajaguru2, Ajin R Nair2

  • 1Department of Information Technology, Bannari Amman Institute of Technology, Sathyamangalam 638401, India.

Bioengineering (Basel, Switzerland)
|April 27, 2024
PubMed
Summary

This study enhances cancer classification using gene expression data by applying Fast Fourier Transform (FFT) and Dragonfly optimization. The Support Vector Machine (SVM) classifier achieved 98.343% accuracy, improving cancer subtype identification.

Keywords:
Adam and RanAdam tuningDimReFFTMAGE datacancer classificationlung cancer detectionmixture model

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Microarray gene expression analysis is crucial for cancer classification, but databases are complex, noisy, and nonlinear.
  • Extracting meaningful insights from large, redundant microarray datasets presents significant challenges in cancer research.

Purpose of the Study:

  • To develop an effective method for dimensionality reduction and feature selection in microarray data for cancer classification.
  • To improve the accuracy of cancer classification by optimizing machine learning classifiers using advanced techniques.

Main Methods:

  • Employed Fast Fourier Transform (FFT) and Mixture Model (MM) for dimensionality reduction.
  • Utilized the Dragonfly optimization algorithm for feature selection.
  • Evaluated Nonlinear Regression, Naïve Bayes, Decision Tree, Random Forest, and Support Vector Machine (SVM with RBF kernel) classifiers, with and without feature selection.

Main Results:

  • The SVM (RBF) classifier, combined with FFT dimensionality reduction and Dragonfly feature selection, demonstrated superior performance.
  • Hyper-parameter tuning using Random Adaptive Moment Estimation (RanAdam) further improved classifier accuracy.
  • The optimized SVM (RBF) model achieved a highest accuracy of 98.343% in cancer classification.

Conclusions:

  • The integration of FFT, Dragonfly optimization, and RanAdam-tuned SVM (RBF) offers a robust approach for accurate cancer classification from microarray data.
  • This methodology effectively addresses the challenges of noise and nonlinearity in gene expression datasets, enhancing diagnostic potential.