{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T17:06:47Z","timestamp":1769792807311,"version":"3.49.0"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032041593","type":"print"},{"value":"9783032041609","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-04160-9_29","type":"book-chapter","created":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T04:58:07Z","timestamp":1769749087000},"page":"323-335","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Bio-inspired Feature Selection in Multi-modal Machine Learning Applied in the Healthcare Domain: A Systematic Review"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-7129-6847","authenticated-orcid":false,"given":"Deive A.","family":"Leal","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7939-6030","authenticated-orcid":false,"given":"Rafael Marin Machado","family":"de Souza","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3736-6618","authenticated-orcid":false,"given":"Lucas Vinicius Buchelt","family":"Souza","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1862-712X","authenticated-orcid":false,"given":"Marcio","family":"Biczyk","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marcelo","family":"Amorim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3409-4589","authenticated-orcid":false,"given":"Leandro","family":"de Castro","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,31]]},"reference":[{"issue":"6","key":"29_CR1","doi-asserted-by":"publisher","first-page":"399","DOI":"10.1038\/s41592-018-0019-x","volume":"15","author":"N Altman","year":"2018","unstructured":"Altman N, Krzywinski M (2018) The curse(s) of dimensionality. Nat Methods 15(6):399\u2013400","journal-title":"Nat Methods"},{"issue":"2","key":"29_CR2","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1109\/TPAMI.2018.2798607","volume":"41","author":"T Baltru\u0161aitis","year":"2018","unstructured":"Baltru\u0161aitis T, Ahuja C, Morency LP (2018) Multimodal machine learning: a survey and taxonomy. IEEE Trans Pattern Anal Mach Intell 41(2):423\u2013443","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"29_CR3","doi-asserted-by":"crossref","unstructured":"Benkessirat A, Benblidia N (2019) Fundamentals of feature selection: an overview and comparison. In: 2019 IEEE\/ACS 16th international conference on computer systems and applications (AICCSA). IEEE, pp 1\u20136","DOI":"10.1109\/AICCSA47632.2019.9035281"},{"key":"29_CR4","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1007\/s13748-015-0080-y","volume":"5","author":"V Bol\u00f3n-Canedo","year":"2016","unstructured":"Bol\u00f3n-Canedo V, S\u00e1nchez-Maro\u00f1o N, Alonso-Betanzos A (2016) Feature selection for high-dimensional data. Prog Artif Intell 5:65\u201375","journal-title":"Prog Artif Intell"},{"key":"29_CR5","doi-asserted-by":"crossref","unstructured":"Brabazon A, O\u2019Neill M, McGarraghy S (2015) Natural computing algorithms, vol 554. Springer","DOI":"10.1007\/978-3-662-43631-8"},{"issue":"1","key":"29_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-019-0217-0","volume":"6","author":"S Dash","year":"2019","unstructured":"Dash S, Shakyawar SK, Sharma M, Kaushik S (2019) Big data in healthcare: management, analysis and future prospects. J Big Data 6(1):1\u201325","journal-title":"J Big Data"},{"key":"29_CR7","unstructured":"De Jong KA, Fogel L, Schwefel HP (1997) The handbook of evolutionary computation. IOP Publishing Ltd and Oxford University Press"},{"key":"29_CR8","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s00530-015-0494-1","volume":"23","author":"L Gao","year":"2017","unstructured":"Gao L, Song J, Liu X, Shao J, Liu J, Shao J (2017) Learning in high-dimensional multimedia data: the state of the art. Multimed Syst 23:303\u2013313","journal-title":"Multimed Syst"},{"issue":"2","key":"29_CR9","doi-asserted-by":"publisher","first-page":"1849","DOI":"10.1007\/s00521-022-07767-4","volume":"35","author":"DS Irene","year":"2023","unstructured":"Irene DS, Lakshmi M, Kinol AMJ, Kumar AJS (2023) Improved deep convolutional neural network-based coot optimization for multimodal disease risk prediction. Neural Comput Appl 35(2):1849\u20131862","journal-title":"Neural Comput Appl"},{"key":"29_CR10","doi-asserted-by":"crossref","unstructured":"Jiao T, Guo C, Feng X, Chen Y, Song J (2024) A comprehensive survey on deep learning multi-modal fusion: methods, technologies and applications. Comput Mater Contin 80(1)","DOI":"10.32604\/cmc.2024.053204"},{"issue":"10","key":"29_CR11","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1145\/1400181.1400200","volume":"51","author":"L Kari","year":"2008","unstructured":"Kari L, Rozenberg G (2008) The many facets of natural computing. Commun ACM 51(10):72\u201383","journal-title":"Commun ACM"},{"key":"29_CR12","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.inffus.2019.06.008","volume":"53","author":"D Kelly","year":"2020","unstructured":"Kelly D, Condell J, Curran K, Caulfield B (2020) A multimodal smartphone sensor system for behaviour measurement and health status inference. Inf Fusion 53:43\u201354","journal-title":"Inf Fusion"},{"issue":"9","key":"29_CR13","doi-asserted-by":"publisher","first-page":"1449","DOI":"10.1109\/JPROC.2015.2460697","volume":"103","author":"D Lahat","year":"2015","unstructured":"Lahat D, Adali T, Jutten C (2015) Multimodal data fusion: an overview of methods, challenges, and prospects. Proc IEEE 103(9):1449\u20131477","journal-title":"Proc IEEE"},{"issue":"6","key":"29_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3136625","volume":"50","author":"J Li","year":"2017","unstructured":"Li J, Cheng K, Wang S, Morstatter F, Trevino RP, Tang J, Liu H (2017) Feature selection: a data perspective. ACM Comput Surv (CSUR) 50(6):1\u201345","journal-title":"ACM Comput Surv (CSUR)"},{"key":"29_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.106930","volume":"159","author":"D Lu","year":"2023","unstructured":"Lu D, Yue Y, Hu Z, Xu M, Tong Y, Ma H (2023) Effective detection of Alzheimer\u2019s disease by optimizing fuzzy K-nearest neighbors based on salp swarm algorithm. Comput Biol Med 159:106930","journal-title":"Comput Biol Med"},{"key":"29_CR16","doi-asserted-by":"crossref","unstructured":"Michalewicz Z (1996) Genetic algorithms + data structures = evolution programs. Springer-Verlag Berlin Heidelberg GmbH","DOI":"10.1007\/978-3-662-03315-9"},{"key":"29_CR17","doi-asserted-by":"crossref","unstructured":"Mohamed AW, Oliva D, Suganthan PN (2022) Handbook of nature-inspired optimization algorithms: the state of the art. Springer","DOI":"10.1007\/978-3-031-07516-2"},{"key":"29_CR18","first-page":"1","volume":"9","author":"HF Nweke","year":"2019","unstructured":"Nweke HF, Teh YW, Mujtaba G, Alo UR, Al-garadi MA (2019) Multi-sensor fusion based on multiple classifier systems for human activity identification. HCIS 9:1\u201344","journal-title":"HCIS"},{"key":"29_CR19","doi-asserted-by":"publisher","first-page":"65725","DOI":"10.1109\/ACCESS.2023.3289403","volume":"11","author":"H Qi","year":"2023","unstructured":"Qi H, An Y, Hu X, Miao S, Li J (2023) Explainable machine learning explores association between sarcopenia and breast cancer distant metastasis. IEEE Access 11:65725\u201365738","journal-title":"IEEE Access"},{"issue":"6","key":"29_CR20","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1109\/MSP.2017.2738401","volume":"34","author":"D Ramachandram","year":"2017","unstructured":"Ramachandram D, Taylor GW (2017) Deep multimodal learning: a survey on recent advances and trends. IEEE Signal Process Mag 34(6):96\u2013108","journal-title":"IEEE Signal Process Mag"},{"issue":"1","key":"29_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11042-023-15706-1","volume":"83","author":"EA Sa\u011fba\u015f","year":"2024","unstructured":"Sa\u011fba\u015f EA, Korukoglu S, Ball\u0131 S (2024) Real-time stress detection from smartphone sensor data using genetic algorithm-based feature subset optimization and k-nearest neighbor algorithm. Multimed Tools Appl 83(1):1\u201332","journal-title":"Multimed Tools Appl"},{"key":"29_CR22","doi-asserted-by":"crossref","unstructured":"Singh P, Singh AK, Choudhary P, Singh MK, Azar AT, Haider Z, Tounsi M (2024) Cross-domain image classification via multimodal augmentations and bacterial foraging optimization. Int J Serv Sci Manag Eng Technol (IJSSMET) 15(1):1\u201325","DOI":"10.4018\/IJSSMET.356234"},{"issue":"1","key":"29_CR23","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1038\/s41746-022-00689-4","volume":"5","author":"LR Soenksen","year":"2022","unstructured":"Soenksen LR, Ma Y, Zeng C, Boussioux L, Villalobos Carballo K, Na L, Wiberg HM, Li ML, Fuentes I, Bertsimas D (2022) Integrated multimodal artificial intelligence framework for healthcare applications. npj Digit Med 5(1):149","journal-title":"npj Digit Med"},{"key":"29_CR24","unstructured":"Thrasher J, Devkota A, Siwakotai P, Chivukula R, Poudel P, Hu C, Bhattarai B, Gyawali P (2023) Multimodal federated learning in healthcare: a review. arXiv preprint arXiv:2310.09650"},{"key":"29_CR25","doi-asserted-by":"crossref","unstructured":"Torre-Bastida AI, D\u00edaz-de Arcaya J, Osaba E, Muhammad K, Camacho D, Del Ser J (2021) Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and future directions. Neural Comput Appl 1\u201331","DOI":"10.1007\/s00521-021-06332-9"},{"key":"29_CR26","doi-asserted-by":"crossref","unstructured":"Tugwell P, Tovey D (2021) Prisma 2020","DOI":"10.1016\/j.jclinepi.2021.04.008"},{"key":"29_CR27","doi-asserted-by":"crossref","unstructured":"Wang S, Zheng K, Kong W, Huang R, Liu L, Wen G, Yu Y (2023) Multimodal data fusion based on IGERNNC algorithm for detecting pathogenic brain regions and genes in Alzheimer\u2019s disease. Brief Bioinform 24(1):bbac515","DOI":"10.1093\/bib\/bbac515"}],"container-title":["Lecture Notes in Networks and Systems","Distributed Computing and Artificial Intelligence, 22nd International Conference"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-04160-9_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T04:58:09Z","timestamp":1769749089000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04160-9_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032041593","9783032041609"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04160-9_29","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"value":"2367-3370","type":"print"},{"value":"2367-3389","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"31 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Distributed Computing and Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lille","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 June 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 June 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dcai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.dcai-conference.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}