AUTHOR=Panda Pinakshi , Bisoy Sukant Kishoro , Panigrahi Amrutanshu , Pati Abhilash , Sahu Bibhuprasad , Guo Zheshan , Liu Haipeng , Jain Prince 

TITLE=BIMSSA: enhancing cancer prediction with salp swarm optimization and ensemble machine learning approaches

JOURNAL=Frontiers in Genetics

VOLUME=Volume 15 - 2024

YEAR=2025

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2024.1491602

DOI=10.3389/fgene.2024.1491602

ISSN=1664-8021

ABSTRACT=BackgroundCancer rates are rising rapidly, causing global mortality. According to the World Health Organization (WHO), 9.9 million people died from cancer in 2020. Machine learning (ML) helps identify cancer early, reducing deaths. An ML-based cancer diagnostic model can use the patient’s genetic information, such as microarray data. Microarray data are high dimensional, which can degrade the performance of the ML-based models. For this, feature selection becomes essential.MethodsSwarm Optimization Algorithm (SSA), Improved Maximum Relevance and Minimum Redundancy (IMRMR), and Boruta form the basis of this work’s ML-based model BIMSSA. The BIMSSA model implements a pipelined feature selection method to effectively handle high-dimensional microarray data. Initially, Boruta and IMRMR were applied to extract relevant gene expression aspects. Then, SSA was implemented to optimize feature size. To optimize feature space, five separate machine learning classifiers, Support Vector Machine (SVM), Random Forest (RF), Extreme Learning Machine (ELM), AdaBoost, and XGBoost, were applied as the base learners. Then, majority voting was used to build an ensemble of the top three algorithms. The ensemble ML-based model BIMSSA was evaluated using microarray data from four different cancer types: Adult acute lymphoblastic leukemia and Acute myelogenous leukemia (ALL-AML), Lymphoma, Mixed-lineage leukemia (MLL), and Small round blue cell tumors (SRBCT).ResultsIn terms of accuracy, the proposed BIMSSA (Boruta + IMRMR + SSA) achieved 96.7% for ALL-AML, 96.2% for Lymphoma, 95.1% for MLL, and 97.1% for the SRBCT cancer datasets, according to the empirical evaluations.ConclusionThe results show that the proposed approach can accurately predict different forms of cancer, which is useful for both physicians and researchers.