{"id":"https://openalex.org/W7204170010","doi":"https://doi.org/10.1007/s44443-026-01082-x","title":"Heterogeneous stacking framework with adaptive Borderline-SMOTE for imbalanced binary classification: a multi-institutional validation study","display_name":"Heterogeneous stacking framework with adaptive Borderline-SMOTE for imbalanced binary classification: a multi-institutional validation study","publication_year":2026,"publication_date":"2026-08-25","ids":{"openalex":"https://openalex.org/W7204170010","doi":"https://doi.org/10.1007/s44443-026-01082-x"},"language":"en","primary_location":{"id":"doi:10.1007/s44443-026-01082-x","doi":"https://doi.org/10.1007/s44443-026-01082-x","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s44443-026-01082-x","pdf_url":"https://link.springer.com/content/pdf/10.1007/s44443-026-01082-x.pdf","source":{"id":"https://openalex.org/S2764955546","display_name":"Journal of King Saud University - Computer and Information Sciences","issn_l":"1319-1578","issn":["1319-1578","2213-1248"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","dongbi-c","jufo-1","norway-1","poland-100"],"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"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":"Journal of King Saud University Computer and Information Sciences","raw_type":"journal-article"},"volume":null,"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://link.springer.com/content/pdf/10.1007/s44443-026-01082-x.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100354683","display_name":"Yang Zhang","orcid":"https://orcid.org/0000-0002-4170-4798","observed_orcids":["https://orcid.org/0000-0002-4170-4798"]},"institutions":[{"id":"https://openalex.org/I167383011","display_name":"Henan University of Science and Technology","ror":"https://ror.org/05d80kz58","country_code":"CN","type":"education","lineage":["https://openalex.org/I167383011"]},{"id":"https://openalex.org/I4210134906","display_name":"First Affiliated Hospital of Henan University of Science and Technology","ror":"https://ror.org/035zbbv42","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210134906"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Zhang","raw_affiliation_strings":["Department of Urology Surgery, The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, 471003, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Urology Surgery, The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, 471003, China","institution_ids":["https://openalex.org/I167383011","https://openalex.org/I4210134906"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101966252","display_name":"Yanping Zhu","orcid":"https://orcid.org/0000-0002-7159-338X","observed_orcids":["https://orcid.org/0000-0002-7159-338X"]},"institutions":[{"id":"https://openalex.org/I167383011","display_name":"Henan University of Science and Technology","ror":"https://ror.org/05d80kz58","country_code":"CN","type":"education","lineage":["https://openalex.org/I167383011"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanping Zhu","raw_affiliation_strings":["School of Nursing, Henan University of Science and Technology, Luoyang, 471023, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Nursing, Henan University of Science and Technology, Luoyang, 471023, China","institution_ids":["https://openalex.org/I167383011"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102920383","display_name":"Xiaohui Wang","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I167383011","display_name":"Henan University of Science and Technology","ror":"https://ror.org/05d80kz58","country_code":"CN","type":"education","lineage":["https://openalex.org/I167383011"]},{"id":"https://openalex.org/I4210134906","display_name":"First Affiliated Hospital of Henan University of Science and Technology","ror":"https://ror.org/035zbbv42","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210134906"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaohui Wang","raw_affiliation_strings":["Department of Urology Surgery, The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, 471003, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Urology Surgery, The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, 471003, China","institution_ids":["https://openalex.org/I167383011","https://openalex.org/I4210134906"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Song Wen","orcid":"https://orcid.org/0009-0003-0354-5559","observed_orcids":["https://orcid.org/0009-0003-0354-5559"]},"institutions":[{"id":"https://openalex.org/I4210113195","display_name":"Gannan Medical University","ror":"https://ror.org/01tjgw469","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210113195"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Song Wen","raw_affiliation_strings":["Department of Oncology, Ganzhou Cancer Hospital, The Affiliated Cancer Hospital of Gannan Medical University, Ganzhou, 341001, Jiangxi, China"],"raw_orcid":"https://orcid.org/0009-0003-0354-5559","affiliations":[{"raw_affiliation_string":"Department of Oncology, Ganzhou Cancer Hospital, The Affiliated Cancer Hospital of Gannan Medical University, Ganzhou, 341001, Jiangxi, China","institution_ids":["https://openalex.org/I4210113195"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5056729235","display_name":"Pengyi Zheng","orcid":"https://orcid.org/0000-0002-3728-5631","observed_orcids":["https://orcid.org/0000-0002-3728-5631"]},"institutions":[{"id":"https://openalex.org/I167383011","display_name":"Henan University of Science and Technology","ror":"https://ror.org/05d80kz58","country_code":"CN","type":"education","lineage":["https://openalex.org/I167383011"]},{"id":"https://openalex.org/I4210134906","display_name":"First Affiliated Hospital of Henan University of Science and Technology","ror":"https://ror.org/035zbbv42","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210134906"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Pengyi Zheng","raw_affiliation_strings":["Department of Urology Surgery, The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, 471003, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Urology Surgery, The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, 471003, China","institution_ids":["https://openalex.org/I167383011","https://openalex.org/I4210134906"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5056729235"],"corresponding_institution_ids":["https://openalex.org/I167383011","https://openalex.org/I4210113195","https://openalex.org/I4210134906"],"apc_list":{"value":1990,"currency":"USD","value_usd":1990},"apc_paid":{"value":1990,"currency":"USD","value_usd":1990},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.66975148,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"38","issue":"7","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":1.0,"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/T12535","display_name":"Machine Learning and Data Classification","score":9.999999747378752e-05,"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/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.0,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/imbalanced-binary-classification","display_name":"imbalanced binary classification","score":0.9850000143051147},{"id":"https://openalex.org/keywords/borderline-smote","display_name":"Borderline-SMOTE","score":0.9079999923706055},{"id":"https://openalex.org/keywords/oversampling","display_name":"oversampling","score":0.6880000233650208},{"id":"https://openalex.org/keywords/tabular-data","display_name":"tabular data","score":0.9139999747276306},{"id":"https://openalex.org/keywords/risk-stratification","display_name":"risk stratification","score":0.75},{"id":"https://openalex.org/keywords/model-transportability","display_name":"model portability","score":0.5860000252723694},{"id":"https://openalex.org/keywords/external-validation","display_name":"external validation","score":0.6489999890327454},{"id":"https://openalex.org/keywords/shap","display_name":"SHAP","score":0.7919999957084656},{"id":"https://openalex.org/keywords/synthetic-minority-samples","display_name":"synthetic minority samples","score":0.3610000014305115}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.8162000179290771},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6852999925613403},{"id":"https://openalex.org/C150921843","wikidata":"https://www.wikidata.org/wiki/Q1170431","display_name":"Resampling","level":2,"score":0.6615999937057495},{"id":"https://openalex.org/C197323446","wikidata":"https://www.wikidata.org/wiki/Q331222","display_name":"Oversampling","level":3,"score":0.6150000095367432},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6029000282287598},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5666000247001648},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.560699999332428},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.5519000291824341},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.4884999990463257},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.47200000286102295},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.4700999855995178},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.41100001335144043},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.36800000071525574},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.36489999294281006},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.357699990272522},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.329800009727478},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.32679998874664307},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.32199999690055847},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.31790000200271606},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.30390000343322754},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2985000014305115},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.29490000009536743},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.2897000014781952},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.28760001063346863},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.2867000102996826},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.2867000102996826},{"id":"https://openalex.org/C21308566","wikidata":"https://www.wikidata.org/wiki/Q7169365","display_name":"Permutation (music)","level":2,"score":0.2842999994754791},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.28189998865127563},{"id":"https://openalex.org/C207609745","wikidata":"https://www.wikidata.org/wiki/Q4944086","display_name":"Bootstrapping (finance)","level":2,"score":0.2786000072956085},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.2680000066757202},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.26759999990463257},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.26589998602867126},{"id":"https://openalex.org/C27181475","wikidata":"https://www.wikidata.org/wiki/Q541014","display_name":"Cross-validation","level":2,"score":0.26429998874664307},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.2628999948501587},{"id":"https://openalex.org/C136536468","wikidata":"https://www.wikidata.org/wiki/Q1225894","display_name":"Undersampling","level":2,"score":0.25999999046325684},{"id":"https://openalex.org/C137345334","wikidata":"https://www.wikidata.org/wiki/Q7303350","display_name":"Recursive partitioning","level":2,"score":0.257999986410141},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.2515999972820282}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1007/s44443-026-01082-x","doi":"https://doi.org/10.1007/s44443-026-01082-x","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s44443-026-01082-x","pdf_url":"https://link.springer.com/content/pdf/10.1007/s44443-026-01082-x.pdf","source":{"id":"https://openalex.org/S2764955546","display_name":"Journal of King Saud University - Computer and Information Sciences","issn_l":"1319-1578","issn":["1319-1578","2213-1248"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","dongbi-c","jufo-1","norway-1","poland-100"],"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"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":"Journal of King Saud University Computer and Information Sciences","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:8e41d55a346b46e6b8ab49712dd63040","doi":null,"is_oa":false,"landing_page_url":"https://doaj.org/article/8e41d55a346b46e6b8ab49712dd63040","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,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Journal of King Saud University: Computer and Information Sciences, Vol 38, Iss 7 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1007/s44443-026-01082-x","doi":"https://doi.org/10.1007/s44443-026-01082-x","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s44443-026-01082-x","pdf_url":"https://link.springer.com/content/pdf/10.1007/s44443-026-01082-x.pdf","source":{"id":"https://openalex.org/S2764955546","display_name":"Journal of King Saud University - Computer and Information Sciences","issn_l":"1319-1578","issn":["1319-1578","2213-1248"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","dongbi-c","jufo-1","norway-1","poland-100"],"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"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":"Journal of King Saud University Computer and Information Sciences","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.7578999996185303,"display_name":"Reduced inequalities"}],"sustainable_development_goals_aurora":[{"id":"https://metadata.un.org/sdg/10","score":0.7067816853523254,"display_name":"Reduced inequalities"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7204170010.pdf","grobid_xml":"https://content.openalex.org/works/W7204170010.grobid-xml"},"referenced_works_count":39,"referenced_works":["https://openalex.org/W28412257","https://openalex.org/W1678356000","https://openalex.org/W1966716734","https://openalex.org/W1983024255","https://openalex.org/W2005718054","https://openalex.org/W2041120035","https://openalex.org/W2045030989","https://openalex.org/W2053198390","https://openalex.org/W2100483895","https://openalex.org/W2103444700","https://openalex.org/W2111547563","https://openalex.org/W2117792530","https://openalex.org/W2132791018","https://openalex.org/W2148143831","https://openalex.org/W2149696900","https://openalex.org/W2152561234","https://openalex.org/W2155987515","https://openalex.org/W2158872144","https://openalex.org/W2166692714","https://openalex.org/W2282821441","https://openalex.org/W2295598076","https://openalex.org/W2519802255","https://openalex.org/W2772104639","https://openalex.org/W2788941211","https://openalex.org/W2810566057","https://openalex.org/W2897336679","https://openalex.org/W2911964244","https://openalex.org/W2912934387","https://openalex.org/W2934399013","https://openalex.org/W2967478755","https://openalex.org/W2999091210","https://openalex.org/W3023689102","https://openalex.org/W3097158220","https://openalex.org/W3176376866","https://openalex.org/W4239510810","https://openalex.org/W4282942659","https://openalex.org/W4293812076","https://openalex.org/W4295773257","https://openalex.org/W4407868332"],"related_works":[],"abstract_inverted_index":{"Binary":[0],"classification":[1,220,259],"in":[2,10,113,174,218],"imbalanced":[3,52,219],"tabular":[4,258],"datasets":[5],"remains":[6],"a":[7,64,80,104,122],"significant":[8],"challenge":[9],"machine":[11],"learning,":[12],"as":[13],"conventional":[14],"risk-stratification":[15],"models":[16],"exhibit":[17],"limited":[18],"discriminative":[19],"performance":[20],"and":[21,54,79,131,163,185,209,228,240,253],"fail":[22],"to":[23,172],"capture":[24],"nonlinear":[25],"interactions":[26],"among":[27],"heterogeneous":[28,41,65],"features.":[29],"Existing":[30],"approaches":[31],"often":[32],"suffer":[33],"from":[34],"three":[35],"critical":[36],"limitations:":[37],"model-selection":[38],"uncertainty":[39],"across":[40,194],"data":[42],"distributions,":[43],"systematic":[44],"bias":[45],"toward":[46],"majority":[47],"classes":[48],"when":[49],"training":[50],"on":[51,89,121,134,151],"datasets,":[53],"insufficient":[55],"interpretability":[56],"for":[57,215,256],"high-stakes":[58,257],"decision-making":[59],"applications.":[60],"This":[61],"work":[62],"proposes":[63],"stacking":[66],"ensemble":[67,166,216],"framework":[68,118,248],"that":[69,108],"integrates":[70],"five":[71],"complementary":[72],"base":[73],"learners\u2014Random":[74],"Forest,":[75],"XGBoost,":[76],"LightGBM,":[77],"CatBoost,":[78],"Multi-Layer":[81],"Perceptron\u2014through":[82],"an":[83,98,144,250],"L2-regularized":[84],"logistic":[85],"regression":[86],"meta-learner":[87,210],"trained":[88],"out-of-fold":[90],"predictions.":[91],"To":[92],"address":[93],"class":[94,263],"imbalance,":[95],"we":[96],"incorporate":[97],"adaptive":[99],"Borderline-SMOTE":[100],"oversampling":[101],"strategy":[102],"within":[103],"leakage-free":[105],"cross-validation":[106],"pipeline":[107],"concentrates":[109],"synthetic":[110],"sample":[111],"generation":[112],"high-difficulty":[114],"borderline":[115],"regions.":[116],"The":[117,140,246],"is":[119],"developed":[120],"multi-institutional":[123],"dataset":[124],"of":[125,146,170,183,197],"4,127":[126],"instances":[127],"with":[128,181,243,261],"28":[129],"features":[130,239],"externally":[132],"validated":[133],"two":[135],"independent":[136],"cohorts":[137],"(n=612,":[138],"n=489).":[139],"proposed":[141,247],"approach":[142],"achieves":[143],"AUC":[145],"0.892":[147],"(95%":[148],"CI:":[149],"0.876\u20130.908)":[150],"the":[152,157,236],"internal":[153],"test":[154],"set,":[155],"outperforming":[156],"strongest":[158],"single":[159],"learner":[160],"by":[161,168],"2.8%":[162],"four":[164],"state-of-the-art":[165],"baselines":[167],"margins":[169],"1.3%":[171],"3.4%":[173],"AUC.":[175],"External":[176],"validation":[177],"demonstrates":[178],"robust":[179],"transportability":[180],"AUCs":[182],"0.871":[184],"0.858":[186],"respectively.":[187],"Decision-curve":[188],"analysis":[189],"confirms":[190],"superior":[191],"net":[192],"benefit":[193],"threshold":[195],"ranges":[196],"10\u201340%.":[198],"Comprehensive":[199],"ablation":[200],"studies":[201],"covering":[202],"feature-group":[203],"contributions,":[204],"base-learner":[205],"combinations,":[206],"resampling":[207],"strategies,":[208],"architectures":[211],"provide":[212],"empirical":[213],"guidance":[214],"design":[217],"tasks.":[221],"Model":[222],"decisions":[223],"are":[224],"explained":[225],"at":[226],"global":[227],"instance":[229],"levels":[230],"using":[231],"SHAP":[232],"value":[233],"analysis,":[234],"revealing":[235],"dominant":[237],"predictive":[238],"demonstrating":[241],"consistency":[242],"domain":[244],"knowledge.":[245],"offers":[249],"accurate,":[251],"interpretable,":[252],"generalizable":[254],"solution":[255],"problems":[260],"inherent":[262],"imbalance.":[264]},"counts_by_year":[],"updated_date":"2026-10-12T09:40:04.860316","created_date":"2026-08-26T00:00:00"}
