Machine learning-based leukemia classification using gene expression for accurate diagnosis

Mahmoud, Amena and Talpur, Kazim Raza and Saini, Shilpa and Talpur, Bandeh Ali and Shah, Asadullah and Zaki, John (2025) Machine learning-based leukemia classification using gene expression for accurate diagnosis. In: IEEE 9TH ICETAS 2024, November 20-22, Bahrain.

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Abstract

The accurate classification of leukemia is of utmost importance in order to develop effective treatment strategies, given its complex and heterogeneous nature, encompassing numerous subtypes. Given the availability of gene expression data, machine learning algorithms have demonstrated considerable promise in recent years for enhancing the accuracy of leukemia classification. Machine leraning techniques have been used for gene expression dataset in order to classify the existence of Leukemia. A dataset has been taken and thus preprocessed which consist of profiles of gene expression of patients having leukemia. Then the feature selection techniques have been used for classification of informative genes. Various ML techniques have been used here for classification. The proposed techniques is then compared with the existing approaches using the same dataset. It is observed that the proposed model for leukemia classification has an accuracy of 97% using SVM algorithm whereas 94% is using Logistic regression algorithm.

Item Type:	Proceeding Paper (Other)
Uncontrolled Keywords:	ANN, KNN, Gene Expression, Logistic Regression, Machine learning, support vector machine.
Subjects:	T Technology > T Technology (General) > T10.5 Communication of technical information
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology > Department of Information System Kulliyyah of Information and Communication Technology > Department of Information System
Depositing User:	Prof Asadullah Shah
Date Deposited:	17 Sep 2025 10:50
Last Modified:	17 Sep 2025 16:02
URI:	http://irep.iium.edu.my/id/eprint/123205

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