[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125117-en":3,"doc-seo-125117-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125117,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","BIMSSA - enhancing cancer prediction with salp swarm optimization and ensemble machine learning approaches","Cancer incidence and mortality are rising worldwide, making early and accurate diagnosis essential. Machine learning can support early cancer detection by leveraging patient genetic information such as microarray data, but the high-dimensional feature space often degrades model performance. The proposed BIMSSA pipeline combines Boruta and IMRMR for relevant gene selection and uses SSA to optimize feature size, then evaluates multiple classifiers with an ensemble majority-voting strategy.","TYPE Original Research PUBLISHED 06 January 2025  \nDOI 10.3389/fgene.2024.1491602  \nOPEN ACCESS  \nEDITED BY  \nLei Chen,  \nShanghai Maritime University, China  \nREVIEWED BY  \nPuspanjali Mohapatra,  \nInternational Institute of Information Technology, India  \nAbdelkader Benyettou,  \nCentre Universitaire de Relizane, Algeria  \n*CORRESPONDENCE  \nZheshan Guo,  \n [guozheshan@hainanu.edu.cn](guozheshan@hainanu.edu.cn)[ ](guozheshan@hainanu.edu.cn)Prince Jain,  \n [princeece48@gmail.com](princeece48@gmail.com)  \nRECEIVED 06 October 2024  \nACCEPTED 11 December 2024  \nPUBLISHED 06 January 2025  \nCITATION  \nPanda P, Bisoy SK, Panigrahi A, Pati A, Sahu B, Guo Z, Liu H and Jain P (2025) BIMSSA: enhancing cancer prediction with salp swarm optimization and ensemble machine learning approaches.  \nFront. Genet. 15:1491602 .  \ndoi: 10.3389/fgene.2024.1491602  \nCOPYRIGHT  \n© 2025 Panda, Bisoy, Panigrahi, Pati, Sahu, Guo, Liu and Jain. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nBIMSSA: enhancing cancer prediction with salp swarm optimization and ensemble machine learning approaches  \nPinakshi Panda 1, Sukant Kishoro Bisoy 1, Amrutanshu Panigrahi 2, Abhilash Pati 2, Bibhuprasad Sahu 3, Zheshan Guo 4*,  \nHaipeng Liu 􀀁 5 and Prince Jain 􀀁 6*  \n1Department of Computer Science and Engineering, C. V. Raman Global University, Bhubaneswar, Odisha, India, 2Department of Computer Science and Engineering, Siksha ‘O’ Anusandhan (Deemed tobe University), Bhubaneswar, Odisha, India, 3Department of Information Technology, Vardhaman College of Engineering (Autonomous), Hyderabad, Telangana, India, 4Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Sanya, China, 5Centre for Intelligent Healthcare, Coventry University, Coventry, United Kingdom, 6Department of Mechatronics Engineering, Parul Institute of Technology, Parul University, Vadodara, Gujarat, India  \nBackground: Cancer 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 MLbased 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.  \nMethods: Swarm Optimization Algorithm (SSA), Improved Maximum Relevance and Minimum Redundancy (IMRMR), and Boruta form the basis of this work’s MLbased model BIMSSA. The BIMSSA model implements a pipelined featureselection 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) .  \nResults: In 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 evalu","cbCaif1vqjFzt7ca","https://ap.wps.com/l/cbCaif1vqjFzt7ca","pdf",3990206,1,22,"English","en",105,"# Introduction\n## Cancer burden and need for early diagnosis\n## Microarray data and feature selection challenges\n# Methods\n## BIMSSA pipeline: Boruta and IMRMR\n## SSA-based feature size optimization\n## Ensemble learning with multiple classifiers\n# Results\n## Accuracy across cancer types\n# Conclusion","[{\"question\":\"What problem does BIMSSA address in cancer prediction?\",\"answer\":\"BIMSSA targets the difficulty of using high-dimensional microarray gene-expression data for machine learning-based cancer diagnosis, where irrelevant or redundant features can reduce performance.\"},{\"question\":\"How does BIMSSA perform feature selection and optimization?\",\"answer\":\"It first applies Boruta and IMRMR to extract relevant gene expression aspects, then uses SSA to optimize the feature size before model training.\"},{\"question\":\"Which models are used and how is the final ensemble built?\",\"answer\":\"BIMSSA trains five base learners—SVM, Random Forest, Extreme Learning Machine, AdaBoost, and XGBoost—and then combines the top three using majority voting to form the ensemble.\"}]","BIMSSA - enhancing cancer prediction with salp swarm optimization and ensemble machine learning approaches | PDF",1785896741,55,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"bimssa-enhancing-cancer-prediction-with-salp-swarm-optimization-and-ensemble-machine-learning-approaches","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/bimssa-enhancing-cancer-prediction-with-salp-swarm-optimization-and-ensemble-machine-learning-approaches/125117/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does BIMSSA address in cancer prediction?","Question",{"text":75,"@type":76},"BIMSSA targets the difficulty of using high-dimensional microarray gene-expression data for machine learning-based cancer diagnosis, where irrelevant or redundant features can reduce performance.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does BIMSSA perform feature selection and optimization?",{"text":80,"@type":76},"It first applies Boruta and IMRMR to extract relevant gene expression aspects, then uses SSA to optimize the feature size before model training.",{"name":82,"@type":73,"acceptedAnswer":83},"Which models are used and how is the final ensemble built?",{"text":84,"@type":76},"BIMSSA trains five base learners—SVM, Random Forest, Extreme Learning Machine, AdaBoost, and XGBoost—and then combines the top three using majority voting to form the ensemble.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]