Innovative Methods Enhance Lung Cancer Classification

Wednesday, 17 July 2024, 04:28

This research explores the impact of employing STFT, LASSO, and EHO for effective feature extraction in lung cancer classification. By addressing the curse of dimensionality issue in microarray gene expression data, these methods significantly enhance classification accuracy and interpretability. The study demonstrates the superiority of EHO feature extraction with the FPO-GMM classifier, achieving high accuracy and performance metrics.

Nature — Innovative Methods Enhance Lung Cancer Classification

Challenge of Microarray Gene Expression Data

The microarray gene expression data presents a challenge due to the curse of dimensionality problem, causing issues with overfitting and reduced classification accuracy.

Significance of Feature Extraction Methods

STFT, LASSO, and EHO are utilized to extract meaningful information and reduce the dimensionality of the microarray gene expression data.

Impact on Lung Cancer Classification

By employing innovative methods like EHO feature extraction and FPO-GMM classifier, the research achieves high accuracy, F1 score, MCC, and Kappa values.

This article was prepared using information from open sources in accordance with the principles of Ethical Policy. The editorial team is not responsible for absolute accuracy, as it relies on data from the sources referenced.

Challenge of Microarray Gene Expression Data

Significance of Feature Extraction Methods

Impact on Lung Cancer Classification

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