Measuring Comparative Statistical Effectiveness of Cancer Subtype Categorization Using Gene Expression Data

Avila Clemenshia P.; Deepa C.

doi:10.24423/cames.2024.555

Avila Clemenshia P. Department of Computer Science, Sri Ramakrishna College of Arts & Science, India
Deepa C. Department of Computer Science, Sri Ramakrishna College of Arts & Science, India

DOI: http://dx.doi.org/10.24423/cames.2024.555

Abstract

This work focused on the analysis of various gene expression-based cancer subtype classification approaches. Correctly classifying cancer subtypes is critical for understanding cancer pathophysiology and effectively treating cancer patients by using gene expression data to categorize cancer subtypes. When dealing with limited samples and high-dimensional biological data, most classifiers may suffer from overfitting and lower precision. The goal of this research is to develop a machine learning (ML) system capable of classifying human cancer subtypes based on gene expression data in cancer cells. These issues can be solved using ML algorithms such as Transductive Support Vector Machines (TSVM), Boosting Cascade Deep Forest (BCD Forest), Enhanced Neural Network Classifier (ENNC), Deep Flexible Neural Forest (DFN Forest), Convolutional Neural Network (CNN), and Cascade Flexible Neural Forest (CFN Forest). In inferring the benefits and rawbacks of these strategies, such as DFN Forest and CFN Forest, the findings are 95%.

Keywords

cancer subtypes, gene expression data, machine learning, Deep Flexible Neural Forest (DFN Forest) strategy,

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