{"id":"https://openalex.org/W4408788358","doi":"https://doi.org/10.1186/s13040-025-00440-1","title":"Advancing preeclampsia prediction: a tailored machine learning pipeline integrating resampling and ensemble models for handling imbalanced medical data","display_name":"Advancing preeclampsia prediction: a tailored machine learning pipeline integrating resampling and ensemble models for handling imbalanced medical data","publication_year":2025,"publication_date":"2025-03-24","ids":{"openalex":"https://openalex.org/W4408788358","doi":"https://doi.org/10.1186/s13040-025-00440-1","pmid":"https://pubmed.ncbi.nlm.nih.gov/40128863"},"language":"en","primary_location":{"id":"doi:10.1186/s13040-025-00440-1","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s13040-025-00440-1","pdf_url":"https://biodatamining.biomedcentral.com/counter/pdf/10.1186/s13040-025-00440-1","source":{"id":"https://openalex.org/S84409260","display_name":"BioData Mining","issn_l":"1756-0381","issn":["1756-0381"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320256","host_organization_name":"BioMed Central","host_organization_lineage":["https://openalex.org/P4310320256","https://openalex.org/P4310319965"],"host_organization_lineage_names":["BioMed Central","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"BioData Mining","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://biodatamining.biomedcentral.com/counter/pdf/10.1186/s13040-025-00440-1","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5046357184","display_name":"Yinyao Ma","orcid":null},"institutions":[{"id":"https://openalex.org/I4210115919","display_name":"The People's Hospital of Guangxi Zhuang Autonomous Region","ror":"https://ror.org/02aa8kj12","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210115919"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yinyao Ma","raw_affiliation_strings":["Department of Obstetrics, People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, 530016, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Obstetrics, People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, 530016, China","institution_ids":["https://openalex.org/I4210115919"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038754214","display_name":"Hanlin Lv","orcid":"https://orcid.org/0000-0002-1876-7846"},"institutions":[{"id":"https://openalex.org/I4387153646","display_name":"BGI Research","ror":"https://ror.org/05gsxrt27","country_code":null,"type":"nonprofit","lineage":["https://openalex.org/I100135526","https://openalex.org/I4387153646"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hanlin Lv","raw_affiliation_strings":["BGI Research, Wuhan, 430074, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"BGI Research, Wuhan, 430074, China","institution_ids":["https://openalex.org/I4387153646"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042000178","display_name":"Yanhua Ma","orcid":"https://orcid.org/0000-0002-2254-3896"},"institutions":[{"id":"https://openalex.org/I4210115919","display_name":"The People's Hospital of Guangxi Zhuang Autonomous Region","ror":"https://ror.org/02aa8kj12","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210115919"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanhua Ma","raw_affiliation_strings":["Department of Obstetrics, People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, 530016, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Obstetrics, People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, 530016, China","institution_ids":["https://openalex.org/I4210115919"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112719601","display_name":"Xiao Wang","orcid":"https://orcid.org/0000-0002-0008-0659"},"institutions":[{"id":"https://openalex.org/I4387153646","display_name":"BGI Research","ror":"https://ror.org/05gsxrt27","country_code":null,"type":"nonprofit","lineage":["https://openalex.org/I100135526","https://openalex.org/I4387153646"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiao Wang","raw_affiliation_strings":["BGI Research, Wuhan, 430074, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"BGI Research, Wuhan, 430074, China","institution_ids":["https://openalex.org/I4387153646"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5093889367","display_name":"Longting Lv","orcid":"https://orcid.org/0009-0008-6009-4922"},"institutions":[{"id":"https://openalex.org/I4387153646","display_name":"BGI Research","ror":"https://ror.org/05gsxrt27","country_code":null,"type":"nonprofit","lineage":["https://openalex.org/I100135526","https://openalex.org/I4387153646"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Longting Lv","raw_affiliation_strings":["BGI Research, Wuhan, 430074, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"BGI Research, Wuhan, 430074, China","institution_ids":["https://openalex.org/I4387153646"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100965843","display_name":"Xuxia Liang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210115919","display_name":"The People's Hospital of Guangxi Zhuang Autonomous Region","ror":"https://ror.org/02aa8kj12","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210115919"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuxia Liang","raw_affiliation_strings":["Department of Obstetrics, People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, 530016, China. 134506734@qq.com","Department of Obstetrics, People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, 530016, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Obstetrics, People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, 530016, China. 134506734@qq.com","institution_ids":[]},{"raw_affiliation_string":"Department of Obstetrics, People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, 530016, China","institution_ids":["https://openalex.org/I4210115919"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100436176","display_name":"Lei Wang","orcid":"https://orcid.org/0009-0005-2757-1748"},"institutions":[{"id":"https://openalex.org/I100135526","display_name":"BGI Group (China)","ror":"https://ror.org/045pn2j94","country_code":"CN","type":"company","lineage":["https://openalex.org/I100135526"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Lei Wang","raw_affiliation_strings":["BGI Research, Wuhan, 430074, China. wanglei12@genomics.cn","Guangdong Bigdata Engineering Technology Research Center for Life Sciences, BGI Research, Shenzhen, 518083, China. wanglei12@genomics.cn","Guangdong Bigdata Engineering Technology Research Center for Life Sciences, BGI Research, Shenzhen, 518083, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"BGI Research, Wuhan, 430074, China. wanglei12@genomics.cn","institution_ids":[]},{"raw_affiliation_string":"Guangdong Bigdata Engineering Technology Research Center for Life Sciences, BGI Research, Shenzhen, 518083, China. wanglei12@genomics.cn","institution_ids":[]},{"raw_affiliation_string":"Guangdong Bigdata Engineering Technology Research Center for Life Sciences, BGI Research, Shenzhen, 518083, China","institution_ids":["https://openalex.org/I100135526"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5100436176"],"corresponding_institution_ids":["https://openalex.org/I100135526"],"apc_list":{"value":1690,"currency":"GBP","value_usd":2072},"apc_paid":{"value":1690,"currency":"GBP","value_usd":2072},"fwci":3.2671,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.89273743,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":"18","issue":"1","first_page":"25","last_page":"25"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10290","display_name":"Pregnancy and preeclampsia studies","score":0.4230000078678131,"subfield":{"id":"https://openalex.org/subfields/2729","display_name":"Obstetrics and Gynecology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T10290","display_name":"Pregnancy and preeclampsia studies","score":0.4230000078678131,"subfield":{"id":"https://openalex.org/subfields/2729","display_name":"Obstetrics and Gynecology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.09449999779462814,"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"}},{"id":"https://openalex.org/T11389","display_name":"Maternal and fetal healthcare","score":0.065700002014637,"subfield":{"id":"https://openalex.org/subfields/2735","display_name":"Pediatrics, Perinatology and Child Health"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.7463269829750061},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7021516561508179},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.6380502581596375},{"id":"https://openalex.org/keywords/resampling","display_name":"Resampling","score":0.6326439380645752},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6061870455741882},{"id":"https://openalex.org/keywords/ensemble-forecasting","display_name":"Ensemble forecasting","score":0.5007665157318115},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4891231656074524},{"id":"https://openalex.org/keywords/preeclampsia","display_name":"Preeclampsia","score":0.46482375264167786},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.44581156969070435},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.41384726762771606},{"id":"https://openalex.org/keywords/pregnancy","display_name":"Pregnancy","score":0.08559468388557434}],"concepts":[{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.7463269829750061},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7021516561508179},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.6380502581596375},{"id":"https://openalex.org/C150921843","wikidata":"https://www.wikidata.org/wiki/Q1170431","display_name":"Resampling","level":2,"score":0.6326439380645752},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6061870455741882},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.5007665157318115},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4891231656074524},{"id":"https://openalex.org/C2777218350","wikidata":"https://www.wikidata.org/wiki/Q61335","display_name":"Preeclampsia","level":3,"score":0.46482375264167786},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.44581156969070435},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.41384726762771606},{"id":"https://openalex.org/C2779234561","wikidata":"https://www.wikidata.org/wiki/Q11995","display_name":"Pregnancy","level":2,"score":0.08559468388557434},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1186/s13040-025-00440-1","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s13040-025-00440-1","pdf_url":"https://biodatamining.biomedcentral.com/counter/pdf/10.1186/s13040-025-00440-1","source":{"id":"https://openalex.org/S84409260","display_name":"BioData Mining","issn_l":"1756-0381","issn":["1756-0381"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320256","host_organization_name":"BioMed Central","host_organization_lineage":["https://openalex.org/P4310320256","https://openalex.org/P4310319965"],"host_organization_lineage_names":["BioMed Central","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"BioData Mining","raw_type":"journal-article"},{"id":"pmid:40128863","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/40128863","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"BioData mining","raw_type":null},{"id":"pmh:oai:doaj.org/article:01668f36a69c4100a5a2bbf7cfd54657","is_oa":true,"landing_page_url":"https://doaj.org/article/01668f36a69c4100a5a2bbf7cfd54657","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"BioData Mining, Vol 18, Iss 1, Pp 1-16 (2025)","raw_type":"article"},{"id":"pmh:oai:pubmedcentral.nih.gov:11934807","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/11934807","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"BioData Min","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.1186/s13040-025-00440-1","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s13040-025-00440-1","pdf_url":"https://biodatamining.biomedcentral.com/counter/pdf/10.1186/s13040-025-00440-1","source":{"id":"https://openalex.org/S84409260","display_name":"BioData Mining","issn_l":"1756-0381","issn":["1756-0381"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320256","host_organization_name":"BioMed Central","host_organization_lineage":["https://openalex.org/P4310320256","https://openalex.org/P4310319965"],"host_organization_lineage_names":["BioMed Central","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"BioData Mining","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4637923021","display_name":null,"funder_award_id":"AB22035056","funder_id":"https://openalex.org/F4320336589","funder_display_name":"Guangxi Key Research and Development Program"},{"id":"https://openalex.org/G5052648834","display_name":null,"funder_award_id":"82060805","funder_id":"https://openalex.org/F4320327720","funder_display_name":"Foundation for Innovative Research Groups of the National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320327720","display_name":"Foundation for Innovative Research Groups of the National Natural Science Foundation of China","ror":null},{"id":"https://openalex.org/F4320336589","display_name":"Guangxi Key Research and Development Program","ror":null}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4408788358.pdf"},"referenced_works_count":50,"referenced_works":["https://openalex.org/W1154833837","https://openalex.org/W1975775758","https://openalex.org/W1993220166","https://openalex.org/W2082893943","https://openalex.org/W2087240369","https://openalex.org/W2092155853","https://openalex.org/W2099435377","https://openalex.org/W2118978333","https://openalex.org/W2135046866","https://openalex.org/W2148143831","https://openalex.org/W2154706222","https://openalex.org/W2157381327","https://openalex.org/W2168508521","https://openalex.org/W2233974801","https://openalex.org/W2490420619","https://openalex.org/W2592251773","https://openalex.org/W2730504020","https://openalex.org/W2766296277","https://openalex.org/W2800788706","https://openalex.org/W2808436993","https://openalex.org/W2900915766","https://openalex.org/W2909445826","https://openalex.org/W2964551072","https://openalex.org/W2999693235","https://openalex.org/W3010721756","https://openalex.org/W3042661563","https://openalex.org/W3081112584","https://openalex.org/W3136244234","https://openalex.org/W3157699413","https://openalex.org/W3187081201","https://openalex.org/W4200606351","https://openalex.org/W4205590923","https://openalex.org/W4206983411","https://openalex.org/W4210865385","https://openalex.org/W4221120298","https://openalex.org/W4243367342","https://openalex.org/W4247571494","https://openalex.org/W4280491857","https://openalex.org/W4294631339","https://openalex.org/W4296311910","https://openalex.org/W4310350094","https://openalex.org/W4321096112","https://openalex.org/W4321214126","https://openalex.org/W4377091257","https://openalex.org/W4378417908","https://openalex.org/W4378901723","https://openalex.org/W4386215959","https://openalex.org/W4389513437","https://openalex.org/W4390889232","https://openalex.org/W4391809309"],"related_works":["https://openalex.org/W2891633941","https://openalex.org/W3202800081","https://openalex.org/W3101614107","https://openalex.org/W1909207154","https://openalex.org/W4390971112","https://openalex.org/W3036530763","https://openalex.org/W3124390867","https://openalex.org/W1514365828","https://openalex.org/W3204228978","https://openalex.org/W2155806188"],"abstract_inverted_index":{"BACKGROUND:":[0],"Constructing":[1],"a":[2,27,54,113,212,241,263,285],"predictive":[3,33,179,248],"model":[4,122,157,235],"is":[5,284],"challenging":[6],"in":[7,46,62,234,269,277],"imbalanced":[8,48,63,255],"medical":[9,64,270,279],"dataset":[10],"(such":[11],"as":[12,128],"preeclampsia),":[13],"particularly":[14],"when":[15],"employing":[16],"ensemble":[17,36,108],"machine":[18,37,109],"learning":[19,38,110],"algorithms.":[20],"OBJECTIVE:":[21],"This":[22,92,221,238],"study":[23,239],"aims":[24],"to":[25,82,134,152,266],"develop":[26],"robust":[28,242],"pipeline":[29,56,243],"that":[30,244],"enhances":[31,246],"the":[32,41,75,97,136,168,178,189,225,247,260],"performance":[34,125,158,180,249],"of":[35,44,78,100,121,171,175,181,209,215,250,262],"models":[39,251],"for":[40,58,161,195,252,274],"early":[42,59],"prediction":[43,61],"preeclampsia":[45,60,253],"an":[47,205],"dataset.":[49],"METHODS:":[50],"Our":[51,165,257],"research":[52],"establishes":[53,240],"comprehensive":[55],"optimized":[57,206],"datasets.":[65,91,256],"We":[66,117],"gathered":[67],"electronic":[68],"health":[69],"records":[70],"from":[71,80],"pregnant":[72],"women":[73],"at":[74],"People's":[76],"Hospital":[77],"Guangxi":[79],"2015":[81],"2020,":[83],"with":[84,198],"additional":[85],"external":[86],"validation":[87],"using":[88],"three":[89],"public":[90],"extensive":[93],"data":[94],"collection":[95],"facilitated":[96],"systematic":[98,172],"assessment":[99],"various":[101,278],"resampling":[102],"techniques,":[103],"varied":[104],"minority-to-majority":[105,207],"ratios,":[106],"and":[107,132,147,159,204],"algorithms":[111],"through":[112],"structured":[114],"evaluation":[115],"process.":[116],"analyzed":[118],"4,608":[119],"combinations":[120],"settings":[123],"against":[124],"metrics":[126],"such":[127],"G-mean,":[129],"MCC,":[130],"AP,":[131],"AUC":[133],"determine":[135],"most":[137,185],"effective":[138,186],"configurations.":[139],"Advanced":[140],"statistical":[141],"analyses":[142],"including":[143],"OLS":[144],"regression,":[145],"ANOVA,":[146],"Kruskal-Wallis":[148],"tests":[149],"were":[150],"utilized":[151,188],"fine-tune":[153],"these":[154],"settings,":[155],"enhancing":[156],"robustness":[160],"clinical":[162],"application.":[163],"RESULTS:":[164],"analysis":[166],"confirmed":[167],"significant":[169],"impact":[170],"sequential":[173],"optimization":[174,268],"variables":[176],"on":[177],"our":[182],"models.":[183],"The":[184],"configuration":[187,222],"Inverse":[190],"Weighted":[191],"Gaussian":[192],"Mixture":[193],"Model":[194],"resampling,":[196],"combined":[197],"Gradient":[199],"Boosting":[200],"Decision":[201],"Trees":[202],"algorithm,":[203],"ratio":[208],"0.09,":[210],"achieving":[211],"Geometric":[213],"Mean":[214],"0.6694":[216],"(95%":[217],"confidence":[218],"interval:":[219],"0.5855-0.7557).":[220],"significantly":[223,245],"outperformed":[224],"baseline":[226],"across":[227],"all":[228],"evaluated":[229],"metrics,":[230],"demonstrating":[231],"substantial":[232],"improvements":[233],"performance.":[236],"CONCLUSIONS:":[237],"within":[254],"findings":[258],"underscore":[259],"importance":[261],"strategic":[264],"approach":[265],"variable":[267],"diagnostics,":[271],"offering":[272],"potential":[273],"broad":[275],"application":[276],"contexts":[280],"where":[281],"class":[282],"imbalance":[283],"concern.":[286]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}