Northampton Electronic Collection of Theses and Research

Developing a Multidimensional Fuzzy Deep Learning for Cancer Classification Using Gene Expression Data

Khalsan, M., Mu, M., Al-shamery, E. S., Ajit, S., Machado, L. and Opoku Agyeman, M. (2023) Developing a Multidimensional Fuzzy Deep Learning for Cancer Classification Using Gene Expression Data. In: Wyld, D. C. and Nagamalai, D. (eds.) Computer Science & Information Technology (CS & IT) : 9th International Conference on Computer Science, Engineering and Applications (CSEA 2023). ARE: Academy and Industry Research Collaboration Center (AIRCC). 37 - 49.

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Khalsan_et_al_2023_Developing_a_Multidimensional_Fuzzy_Deep_Learning_for_Cancer_Classification_Using_Gene_Expression_Data.pdf - Published Version
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Abstract:
In the realm of cancer research, the identification of biomarker genes plays a pivotal role in accurate classification and diagnosis. This study delves into the intersection of machine learning and gene selection to enhance the precision of biomarker identification for cancer classification. Leveraging advanced computational techniques. In the quest for improved cancer classification, studies face challenges due to high-dimensional gene expression data and limited gene relevance. To address these challenges, we developed a novel multidimensional fuzzy deep learning (MFDL) to select subset of significant genes and using those genes to train the model for better accuracy. MFDL is exploring the integration of fuzzy concepts within filter and wrapper methods to select significant genes and applying a fuzzy classifier to improve cancer classification accuracy. Through rigorous experimentation and validation, six gene expression data used, the findings demonstrated the efficacy of our methodology on diverse cancer datasets. The results underscore the importance of integrative computational methods in deciphering the intricate genomic landscape of cancer and spotlight the potential for improved diagnostic accuracy. The developed model showcased outstanding performance across the six employed datasets, demonstrating an average accuracy of 98%, precision of 98.3%, recall of 97.6%, and an f1-score of 97.8%.
Uncontrolled Keywords:
/dk/atira/pure/sustainabledevelopmentgoals/good_health_and_well_being
Creators:
Khalsan, M., Mu, M., Al-shamery, E. S., Ajit, S., Machado, L. and Opoku Agyeman, M.
Editors:
Wyld, D. C. and Nagamalai, D.
Publisher:
Academy and Industry Research Collaboration Center (AIRCC)
Faculties, Divisions and Institutes:
?? ASSCI ??
Date:
20 December 2023
Date Type:
Publication
Page Range:
37 - 49
Title of Book:
Computer Science & Information Technology (CS & IT) : 9th International Conference on Computer Science, Engineering and Applications (CSEA 2023)
Series Name:
Computer Science & Information Technology (CS & IT)
Volume:
13
Number:
23
Event Title:
9th International Conference on Computer Science, Engineering and Applications
Event Dates:
2023-12-16 - 2024-12-17
Place of Publication:
ARE
Number of Pages:
13
Language:
English
ISBN:
978-1-923107-12-0
Status:
Published / Disseminated
Refereed:
Yes
Related URLs:
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