{"id":"https://openalex.org/W7135077780","doi":"https://doi.org/10.1109/clei67442.2025.11420415","title":"A Framework Proposal for Handling Missing Data Guided by Machine Learning: An Adaptive Threshold for Exclusion vs. Imputation in Clinical Datasets","display_name":"A Framework Proposal for Handling Missing Data Guided by Machine Learning: An Adaptive Threshold for Exclusion vs. Imputation in Clinical Datasets","publication_year":2025,"publication_date":"2025-10-27","ids":{"openalex":"https://openalex.org/W7135077780","doi":"https://doi.org/10.1109/clei67442.2025.11420415"},"language":null,"primary_location":{"id":"doi:10.1109/clei67442.2025.11420415","is_oa":false,"landing_page_url":"https://doi.org/10.1109/clei67442.2025.11420415","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 LI Latin American Computer Conference (CLEI)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5128919970","display_name":"Anne Carolinne Carvalho Galdino","orcid":null},"institutions":[{"id":"https://openalex.org/I3121799822","display_name":"Universidade Federal do Piau\u00ed","ror":"https://ror.org/00kwnx126","country_code":"BR","type":"education","lineage":["https://openalex.org/I3121799822"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Anne Carolinne Carvalho Galdino","raw_affiliation_strings":["PPGCC Universidade Federal do Piauí,Teresina,Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"PPGCC Universidade Federal do Piauí,Teresina,Brazil","institution_ids":["https://openalex.org/I3121799822"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052544532","display_name":"Vin\u00edcius Ponte Machado","orcid":"https://orcid.org/0000-0003-3391-8443"},"institutions":[{"id":"https://openalex.org/I3121799822","display_name":"Universidade Federal do Piau\u00ed","ror":"https://ror.org/00kwnx126","country_code":"BR","type":"education","lineage":["https://openalex.org/I3121799822"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Vin\u00edcius Ponte Machado","raw_affiliation_strings":["PPGCC Universidade Federal do Piauí,Teresina,Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"PPGCC Universidade Federal do Piauí,Teresina,Brazil","institution_ids":["https://openalex.org/I3121799822"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128823485","display_name":"Juan Morysson Viana Marciano","orcid":null},"institutions":[{"id":"https://openalex.org/I3121799822","display_name":"Universidade Federal do Piau\u00ed","ror":"https://ror.org/00kwnx126","country_code":"BR","type":"education","lineage":["https://openalex.org/I3121799822"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Juan Morysson Viana Marciano","raw_affiliation_strings":["PPGCC Universidade Federal do Piauí,Teresina,Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"PPGCC Universidade Federal do Piauí,Teresina,Brazil","institution_ids":["https://openalex.org/I3121799822"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5048720456","display_name":"Dorcas Lamounier Costa","orcid":"https://orcid.org/0000-0001-7115-7978"},"institutions":[{"id":"https://openalex.org/I3121799822","display_name":"Universidade Federal do Piau\u00ed","ror":"https://ror.org/00kwnx126","country_code":"BR","type":"education","lineage":["https://openalex.org/I3121799822"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Dorcas Lamounier Costa","raw_affiliation_strings":["Universidade Federal do Piauí,Departamento Materno-Infantil-CCS,Teresina,Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universidade Federal do Piauí,Departamento Materno-Infantil-CCS,Teresina,Brazil","institution_ids":["https://openalex.org/I3121799822"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I3121799822"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.77667504,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.3702000081539154,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.3702000081539154,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10845","display_name":"Advanced Causal Inference Techniques","score":0.10350000113248825,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.0786999985575676,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.9354000091552734},{"id":"https://openalex.org/keywords/imputation","display_name":"Imputation (statistics)","score":0.7552000284194946},{"id":"https://openalex.org/keywords/flexibility","display_name":"Flexibility (engineering)","score":0.4535999894142151},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.38909998536109924},{"id":"https://openalex.org/keywords/data-quality","display_name":"Data quality","score":0.36550000309944153},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.3176000118255615}],"concepts":[{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.9354000091552734},{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.7552000284194946},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6348999738693237},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5590999722480774},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5130000114440918},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49939998984336853},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.4535999894142151},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.38909998536109924},{"id":"https://openalex.org/C24756922","wikidata":"https://www.wikidata.org/wiki/Q1757694","display_name":"Data quality","level":3,"score":0.36550000309944153},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.3176000118255615},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.2957000136375427},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.27090001106262207},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.2619999945163727},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.2606000006198883},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.25189998745918274}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/clei67442.2025.11420415","is_oa":false,"landing_page_url":"https://doi.org/10.1109/clei67442.2025.11420415","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 LI Latin American Computer Conference (CLEI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.828643798828125,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0,71],"presence":[1],"of":[2,15,20,43,48,58,67,73,80,128],"missing":[3,40,69,81],"data":[4,82],"is":[5,52,113,143],"a":[6,30,34,45,90,101,103,126],"perennial":[7],"challenge":[8,105],"in":[9,106],"clinical":[10,107,116],"datasets,":[11],"compromising":[12],"the":[13,18,50,56,65,78,84,111,139],"quality":[14],"analyses":[16],"and":[17,83,99,135],"performance":[19,57],"predictive":[21,74],"models.":[22,109],"This":[23],"paper":[24],"addresses":[25],"this":[26],"problem":[27],"by":[28],"proposing":[29],"methodology":[31],"to":[32,63,95,122,125],"identify":[33],"threshold":[35],"for":[36,145],"excluding":[37,98],"variables":[38,142],"with":[39,77,115],"data.":[41],"Instead":[42],"using":[44],"fixed":[46],"percentage":[47,79],"absence,":[49],"approach":[51],"based":[53,92],"on":[54,93],"evaluating":[55],"machine":[59],"learning":[60],"models":[61],"trained":[62],"predict":[64],"values":[66],"each":[68],"variable.":[70],"analysis":[72],"capability,":[75],"combined":[76],"correlation":[85],"structure":[86],"among":[87],"variables,":[88],"offers":[89],"criterion":[91],"evidence":[94],"decide":[96],"between":[97,141],"imputing":[100],"variable,":[102],"notorious":[104],"prognostic":[108],"While":[110],"method":[112],"illustrated":[114],"data,":[117,137],"its":[118],"flexibility":[119],"allows":[120],"it":[121],"be":[123],"applied":[124],"variety":[127],"other":[129],"contexts,":[130],"such":[131],"as":[132],"environmental,":[133],"social,":[134],"financial":[136],"where":[138],"interdependence":[140],"crucial":[144],"imputation..":[146]},"counts_by_year":[],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2026-03-13T00:00:00"}