Optimization Enabled Ensemble Learning for Leukemia Classification Using Microarray Data

Title

Optimization Enabled Ensemble Learning for Leukemia Classification Using Microarray Data

Creator

Chaitra, P.C.; Saravana Kumar, R.

Description

Leukemia classification involves identification and categorization of various leukemias, a cluster of blood malignancies influencing white blood cells. Proper classification is crucial for selecting the appropriate treatment modalities and predicting outcomes in patients. Historically, leukemia classification was based on clinical and morphological characteristics, but new developments in genomics like microarray and next-generation sequencing tools have facilitated more accurate molecular classifications. Machine learning (ML) and deep learning (DL) methods have transformed leukemia classification by enabling automation of analysis in large and intricate datasets to ensure more accurate and efficient leukemia subtype classification. The primary goal of this research is to suggest a new leukemia classification method using microarray data. Leukemia microarray data first undergoes preprocessing, after which feature selection is performed through Serial Exponential-Secretary Bird Optimization Algorithm (SE-SBOA). SE-SBOA is an optimization method that embeds the exponential weighted moving average concept (EWMA) into Secretary Bird Optimization Algorithm (SBOA). The method helps to find the best feature subset, improving model performance at lower complexity. Lastly, leukemia classification is done using the proposed ensemble method that combines Graph Neural Network (GNN), Multi-Layer Perceptron (MLP) and Random Forest. Utilizing the advantages of GNN, MLP and Random Forest, the model proposed herein attains higher classification accuracy and proves to outperform traditional methods. Experimental results demonstrate that the SE-SBOA-based Ensemble Learning technique outperformed standard methods, attaining an accuracy of 95.9%, a precision of 96.1%, a recall of 96.2%, and an F1-score of 96.2%. 2025, Innovative Information Science and Technology Research Group. All rights reserved.

Source

Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications;Volume;16;Issue;2;pp.903-926

Date

01-01-2025

Publisher

Innovative Information Science and Technology Research Group

Subject

Deep Learning; Disease Detection; Leukemia Classification; Microarray Data; Optimization Algorithm

Coverage

Chaitra P.C., Computer Science and Engineering, Christ University, Kengeri, India, Computer Science and Engineering, Dayananda Sagar Academy of Technology & Management, Visvesvaraya Technological University, Belagavi, India; Saravana Kumar R., Computer Science and Engineering, Dayananda Sagar Academy of Technology & Management, Visvesvaraya Technological University, Belagavi, India

Rights

All Open Access; Bronze Open Access

Relation

ISSN: 20935374;

Format

online

Language

English

Type

Article

Identifier

https://doi.org/10.58346/JOWUA.2025.I2.056

https://www.scopus.com/pages/publications/105013323002?origin=resultslist