A Systematic Review on Leukemia Detection and Classification Techniques Using Gene Expression

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nThis is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.n

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Year : 2025 [if 2224 equals=””]29/09/2025 at 12:12 PM[/if 2224] | [if 1553 equals=””] Volume : 03 [else] Volume : [/if 1553] | [if 424 equals=”Regular Issue”]Issue : [/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 02 | Page :

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    Veerpal kaur, Dr. Jasvir Singh, Dr. Neelofar Sohi,

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  1. Research Scholar, Assistant Professor, Assistant Professor, Department of Computer Science and Engineering Punjabi University Patiala, Department of Computer Science and Engineering Punjabi University Patiala, Department of Computer Science and Engineering Punjabi University Patiala, Punjab, Punjab, Punjab, India, India, India

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Abstract

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nEarly diagnosis of genetic diseases is crucial for effective treatment, especially in the case of Leukemia, a type of blood cancer characterized by abnormal proliferation of white blood cells. This paper presents a systematic review of recent computational techniques for the detection and classification of Leukemia using gene expression data obtained from DNA microarray analysis. The study explores diverse methodologies including machine learning (ML), deep learning (DL), and bio-inspired algorithms for identifying Leukemia subtypes such as AML (Acute Myeloid Leukemia) and ALL (Acute Lymphoblastic Leukemia). Several models such as Support Vector Machines, k-Nearest Neighbors, Artificial Neural Networks, Deep Neural Networks, and ensemble learning methods have been discussed for their accuracy and effectiveness in handling high-dimensional microarray datasets. Moreover, feature selection techniques like Genetic Algorithms, Particle Swarm Optimization, and recent hybrid models such as ACO-ALO and SCBAO are reviewed for enhancing classification accuracy. Recent advancements also incorporate entropy-based and multi-class feature extraction to improve performance. While these approaches demonstrate high precision in classification, challenges such as limited datasets, computational cost, and clinical validation remain. The review highlights the potential of hybrid and integrative models for robust and scalable Leukemia diagnosis, emphasizing the need for continued research to bridge computational advancements with clinical applicability.nn

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Keywords: Microarray, Gene Expression, Leukemia, Acute myeloid leukemia, Acute Lymphoblastic Leukemia

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nVeerpal kaur, Dr. Jasvir Singh, Dr. Neelofar Sohi. [if 2584 equals=”][226 wpautop=0 striphtml=1][else]A Systematic Review on Leukemia Detection and Classification Techniques Using Gene Expression[/if 2584]. International Journal of Genetic Modifications and Recombinations. 29/09/2025; 03(02):-.

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nVeerpal kaur, Dr. Jasvir Singh, Dr. Neelofar Sohi. [if 2584 equals=”][226 striphtml=1][else]A Systematic Review on Leukemia Detection and Classification Techniques Using Gene Expression[/if 2584]. International Journal of Genetic Modifications and Recombinations. 29/09/2025; 03(02):-. Available from: https://journals.stmjournals.com/ijgmr/article=29/09/2025/view=0

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References

[1]     W. Chen, H. Lu, M. Wang, and C. Fang, “Gene expression data classification using artificial neural network ensembles based on samples filtering,” 2009 International Conference on Artificial Intelligence and Computational Intelligence, AICI 2009, vol. 1, pp. 626–628, 2009, doi:  10.1109/AICI.2009.441.

[2]     H. Kang et al., “Gene expression classifiers for relapse-free survival and minimal residual disease improve risk classification and outcome prediction in pediatric B-precursor acute lymphoblastic leukemia,” Blood, vol. 115, no. 7, pp. 1394–1405, Feb. 2010, doi: 10.1182/BLOOD-2009-05-218560.

[3]    Manoj Kumar, Mohammad Husian, Naveen Upreti  and  Deepti Gupta, “GENETIC ALGORITHM: REVIEW AND APPLICATION,” International Journal of Information Technology and Knowledge Management, July-December 2010, Volume 2, No. 2, pp. 451-454.

[4]  Amit Bhola and Arvind Kumar Tiwari “MACHINE LEARNING BASED APPROACHES FOR CANCER CLASSIFICATION USING GENE EXPRESSION DATA,” Machine Learning and Applications: An International Journal (MLAIJ) Vol.2, No.3/4, December 2015, DOI : 10.5121/mlaij.2015.2401

[5]       H. Alshamlan, G. Badr, and Y. Alohali, “MRMR ABC: A hybrid gene selection algorithm for cancer classification using microarray gene expression profiling,” BioMed Research International, vol. 2015, 2015, doi: 10.1155/2015/604910.

[6]   S. Shahbeig, A. Rahideh, M. S. Helfroush, and K. Kazemi, “Gene selection from large-scale gene expression data based on fuzzy interactive multi-objective binary optimization for medical diagnosis,” Biocybernetics and Biomedical Engineering, vol. 38, no. 2, pp. 313–328, Jan. 2018, doi: 10.1016/J.BBE.2018.02.002.

[7]   N. Alrefai, “Ensemble Machine Learning for Leukemia Cancer Diagnosis based on Microarray Datasets,” 2019. [Online]. Available: http://www.ripublication.com

[8]   Pradeep Kumar Mallick et. al., “Convergent         learning–based model for leukemia classification from gene expression,”2020, Personal and Ubiquitous Computing (2023) 27:1103–1110, https://doi.org/10.1007/s00779-020-01467-3.

[9]   C. Moser et al., “A clinically applicable gene expression–based score predicts resistance to induction treatment in acute myeloid leukemia,” Blood Advances, vol. 5, no. 22, pp. 4752–4761, Nov. 2021, doi: 10.1182/BLOODADVANCES.2021004814.

[10]  Halah Almazrua And Hala Alshamlan “ A Comprehensive Survey of Recent Hybrid Feature Selection Methods in Cancer Microarray Gene Expression Data,” VOLUME 10, 2022, Digital Object Identifier 10.1109/ACCESS.2022.3185226.

[11]      M. A. Elaziz, A. A. Ewees, M. A. A. Al-qaness, L. Abualigah, and R. A. Ibrahim, “Sine–Cosine-Barnacles Algorithm Optimizer with disruption operator for global optimization and automatic data clustering,” Expert Systems with Applications, vol. 207, p. 117993, Nov. 2022, doi: 10.1016/J.ESWA.2022.117993.

[12]      D. S. AbdElminaam, E. H. Houssein, M. Said, D. Oliva, and A. Nabil, “An Efficient Heap-Based     Optimizer for Parameters Identification of Modified Photovoltaic Models,” Ain Shams Engineering Journal, vol. 13, no. 5, p. 101728, Sep. 2022, doi: 10.1016/J.ASEJ.2022.101728.

[13]      E. H. Houssein, D. S. Abdelminaam, H. N. Hassan, M. M. Al-Sayed, and E. Nabil, “A Hybrid    Barnacles Mating Optimizer Algorithm with Support Vector Machines for Gene Selection of Microarray Cancer Classification,” IEEE Access, vol. 9, pp. 64895–64905, 2021, doi: 10.1109/ACCESS.2021.3075942.

[14]     A. Razzaque and D.A. Badholia  “PCA based feature extraction and MPSO based feature selection for gene expression microarray medical data classification,” Measurement: Sensors  31 (2024) 100945, https://doi.org/10.1016/j.measen.2023.100945

[15]  Sharanya Selvaraj et. al., “Super learner model for classifying leukemia through gene expression monitoring,” 2024, Discover Oncology (2024) 15:499  https://doi.org/10.1007/s12672-024-01337-x

[16]    S. D, G. Rajaram, E. R et al., “Enhanced leukemia prediction using hybrid ant colony and ant lion optimization for gene selection and classification” 2025, MethodsX 14 (2025) 103239 https://doi.org/10.1016/j.mex.2025.103239

 

 

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[if 424 not_equal=””][else]Ahead of Print[/if 424] Subscription Review Article

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Volume 03
[if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 02
Received 27/04/2025
Accepted 14/08/2025
Published 29/09/2025
Retracted
Publication Time 155 Days

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