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R : A hybrid machine learning feature selection model—HMLFSM to enhance gene classification applied to multiple colon cancers dataset
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Al-Rajab, Murad, Lu, Joan, Xu, Qiang, Kentour, Mohamed, Sawsa, Ahlam, Shuweikeh, Emad, Joy, Mike and Arasaradnam, Ramesh (2023) R : A hybrid machine learning feature selection model—HMLFSM to enhance gene classification applied to multiple colon cancers dataset. PLOS ONE, 18 (11). e0286791. doi:10.1371/journal.pone.0286791 ISSN 1932-6203.
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Official URL: https://doi.org/10.1371/journal.pone.0286791
Abstract
Colon cancer is a significant global health problem, and early detection is critical for improving survival rates. Traditional detection methods, such as colonoscopies, can be invasive and uncomfortable for patients. Machine Learning (ML) algorithms have emerged as a promising approach for non-invasive colon cancer classification using genetic data or patient demographics and medical history. One approach is to use ML to analyse genetic data, or patient demographics and medical history, to predict the likelihood of colon cancer. However, due to the challenges imposed by variable gene expression and the high dimensionality of cancer-related datasets, traditional transductive ML applications have limited accuracy and risk overfitting. In this paper, we propose a new hybrid feature selection model called HMLFSM–Hybrid Machine Learning Feature Selection Model to improve colon cancer gene classification. We developed a multifilter hybrid model including a two-phase feature selection approach, combining Information Gain (IG) and Genetic Algorithms (GA), and minimum Redundancy Maximum Relevance (mRMR) coupling with Particle Swarm Optimization (PSO). We critically tested our model on three colon cancer genetic datasets and found that the new framework outperformed other models with significant accuracy improvements (95%, ~97%, and ~94% accuracies for datasets 1, 2, and 3 respectively). The results show that our approach improves the classification accuracy of colon cancer detection by highlighting important and relevant genes, eliminating irrelevant ones, and revealing the genes that have a direct influence on the classification process. For colon cancer gene analysis, and along with our experiments and literature review, we found that selective input feature extraction prior to feature selection is essential for improving predictive performance.
Item Type: | Journal Article | ||||||
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||
SWORD Depositor: | Library Publications Router | ||||||
Journal or Publication Title: | PLOS ONE | ||||||
Publisher: | Public Library of Science | ||||||
ISSN: | 1932-6203 | ||||||
Official Date: | 2 November 2023 | ||||||
Dates: |
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Volume: | 18 | ||||||
Number: | 11 | ||||||
Article Number: | e0286791 | ||||||
DOI: | 10.1371/journal.pone.0286791 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||
Date of first compliant deposit: | 6 November 2023 | ||||||
Date of first compliant Open Access: | 6 November 2023 | ||||||
RIOXX Funder/Project Grant: |
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