{"id":"https://openalex.org/W4200376084","doi":"https://doi.org/10.1080/08839514.2021.2014190","title":"Prediction of the Probability and Risk Factors of Early Abdominal Aortic Aneurysm Using the Gradient Boosted Decision Trees Model","display_name":"Prediction of the Probability and Risk Factors of Early Abdominal Aortic Aneurysm Using the Gradient Boosted Decision Trees Model","publication_year":2021,"publication_date":"2021-12-28","ids":{"openalex":"https://openalex.org/W4200376084","doi":"https://doi.org/10.1080/08839514.2021.2014190"},"language":"en","primary_location":{"id":"doi:10.1080/08839514.2021.2014190","is_oa":true,"landing_page_url":"https://doi.org/10.1080/08839514.2021.2014190","pdf_url":"https://www.tandfonline.com/doi/pdf/10.1080/08839514.2021.2014190?needAccess=true","source":{"id":"https://openalex.org/S125501549","display_name":"Applied Artificial Intelligence","issn_l":"0883-9514","issn":["0883-9514","1087-6545"],"is_oa":false,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://www.tandfonline.com/doi/pdf/10.1080/08839514.2021.2014190?needAccess=true","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100759700","display_name":"Song Chen","orcid":"https://orcid.org/0000-0002-8037-3004"},"institutions":[{"id":"https://openalex.org/I44468530","display_name":"Qingdao University of Technology","ror":"https://ror.org/01qzc0f54","country_code":"CN","type":"education","lineage":["https://openalex.org/I44468530"]},{"id":"https://openalex.org/I4401041622","display_name":"Chengdu Technological University","ror":"https://ror.org/04713ex73","country_code":null,"type":"education","lineage":["https://openalex.org/I4401041622"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Song Chen","raw_affiliation_strings":["School of Computer Engineering, Chengdu Technological University, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Engineering, Chengdu Technological University, Chengdu, China","institution_ids":["https://openalex.org/I44468530","https://openalex.org/I4401041622"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5027055199","display_name":"Chuan-jun Liao","orcid":null},"institutions":[{"id":"https://openalex.org/I183519381","display_name":"Capital Medical University","ror":"https://ror.org/013xs5b60","country_code":"CN","type":"education","lineage":["https://openalex.org/I183519381"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Chuan-Jun Liao","raw_affiliation_strings":["Department of Vascular Surgery, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Department of Vascular Surgery, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China","institution_ids":["https://openalex.org/I183519381"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5027055199"],"corresponding_institution_ids":["https://openalex.org/I183519381"],"apc_list":{"value":2195,"currency":"USD","value_usd":2195},"apc_paid":{"value":2195,"currency":"USD","value_usd":2195},"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.27811303,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"36","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10983","display_name":"Aortic aneurysm repair treatments","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"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/T10983","display_name":"Aortic aneurysm repair treatments","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"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/T11930","display_name":"Cardiac, Anesthesia and Surgical Outcomes","score":0.994700014591217,"subfield":{"id":"https://openalex.org/subfields/2705","display_name":"Cardiology and Cardiovascular Medicine"},"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/T12067","display_name":"Renal and Vascular Pathologies","score":0.972000002861023,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"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/abdominal-aortic-aneurysm","display_name":"Abdominal aortic aneurysm","score":0.8489357233047485},{"id":"https://openalex.org/keywords/body-mass-index","display_name":"Body mass index","score":0.5728403925895691},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5171434283256531},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.5149134397506714},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.47840890288352966},{"id":"https://openalex.org/keywords/cardiology","display_name":"Cardiology","score":0.4588845372200012},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.4499644637107849},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.44168171286582947},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.366815447807312},{"id":"https://openalex.org/keywords/aneurysm","display_name":"Aneurysm","score":0.3393891453742981}],"concepts":[{"id":"https://openalex.org/C2779993416","wikidata":"https://www.wikidata.org/wiki/Q2256736","display_name":"Abdominal aortic aneurysm","level":3,"score":0.8489357233047485},{"id":"https://openalex.org/C2780221984","wikidata":"https://www.wikidata.org/wiki/Q131191","display_name":"Body mass index","level":2,"score":0.5728403925895691},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5171434283256531},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.5149134397506714},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.47840890288352966},{"id":"https://openalex.org/C164705383","wikidata":"https://www.wikidata.org/wiki/Q10379","display_name":"Cardiology","level":1,"score":0.4588845372200012},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.4499644637107849},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.44168171286582947},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.366815447807312},{"id":"https://openalex.org/C2776098176","wikidata":"https://www.wikidata.org/wiki/Q189389","display_name":"Aneurysm","level":2,"score":0.3393891453742981}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1080/08839514.2021.2014190","is_oa":true,"landing_page_url":"https://doi.org/10.1080/08839514.2021.2014190","pdf_url":"https://www.tandfonline.com/doi/pdf/10.1080/08839514.2021.2014190?needAccess=true","source":{"id":"https://openalex.org/S125501549","display_name":"Applied Artificial Intelligence","issn_l":"0883-9514","issn":["0883-9514","1087-6545"],"is_oa":false,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Artificial Intelligence","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:994eed8bb16848ec919e0fadd6f08dda","is_oa":true,"landing_page_url":"https://doaj.org/article/994eed8bb16848ec919e0fadd6f08dda","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Applied Artificial Intelligence, Vol 36, Iss 1 (2022)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1080/08839514.2021.2014190","is_oa":true,"landing_page_url":"https://doi.org/10.1080/08839514.2021.2014190","pdf_url":"https://www.tandfonline.com/doi/pdf/10.1080/08839514.2021.2014190?needAccess=true","source":{"id":"https://openalex.org/S125501549","display_name":"Applied Artificial Intelligence","issn_l":"0883-9514","issn":["0883-9514","1087-6545"],"is_oa":false,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.4300000071525574,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4200376084.pdf","grobid_xml":"https://content.openalex.org/works/W4200376084.grobid-xml"},"referenced_works_count":36,"referenced_works":["https://openalex.org/W1794427698","https://openalex.org/W1979911373","https://openalex.org/W1999914149","https://openalex.org/W2019046902","https://openalex.org/W2044274282","https://openalex.org/W2076063813","https://openalex.org/W2083199869","https://openalex.org/W2087785971","https://openalex.org/W2088962464","https://openalex.org/W2147234975","https://openalex.org/W2147602225","https://openalex.org/W2227128625","https://openalex.org/W2462292559","https://openalex.org/W2464319088","https://openalex.org/W2539247882","https://openalex.org/W2605587167","https://openalex.org/W2763446259","https://openalex.org/W2776247479","https://openalex.org/W2787655077","https://openalex.org/W2792336352","https://openalex.org/W2899750879","https://openalex.org/W2947069242","https://openalex.org/W2947510612","https://openalex.org/W2951545848","https://openalex.org/W3000006707","https://openalex.org/W3000708278","https://openalex.org/W3008656412","https://openalex.org/W3024707915","https://openalex.org/W3033939918","https://openalex.org/W3035915748","https://openalex.org/W3042579908","https://openalex.org/W3080183739","https://openalex.org/W3088482463","https://openalex.org/W3089152217","https://openalex.org/W3089724252","https://openalex.org/W3091457661"],"related_works":["https://openalex.org/W4293440023","https://openalex.org/W2050736209","https://openalex.org/W2248024246","https://openalex.org/W2374334911","https://openalex.org/W2064079700","https://openalex.org/W2137190715","https://openalex.org/W4243347386","https://openalex.org/W2024110249","https://openalex.org/W2011744972","https://openalex.org/W2355257259"],"abstract_inverted_index":{"Currently,":[0],"abdominal":[1],"aortic":[2],"aneurysm":[3],"(AAA)":[4],"diagnosis":[5],"mainly":[6],"relies":[7],"on":[8,137],"the":[9,12,42,75,85,89,110,146,151,154,161,167,173],"analysis":[10],"of":[11,88,178,188,194],"image":[13],"data,":[14],"such":[15],"as":[16,95,97,106],"Doppler":[17],"ultrasonic":[18],"and":[19,30,52,77,84,125,175,186],"computed":[20],"tomography":[21],"(CT).":[22],"Once":[23],"AAA":[24,104,144,155],"has":[25,47],"formed,":[26],"it":[27,46],"may":[28],"rupture":[29],"lead":[31,81],"to":[32,58,73,82,93,132,153],"death":[33],"at":[34],"any":[35],"time.":[36],"Surgical":[37],"or":[38],"endovascular":[39],"treatment":[40],"was":[41],"only":[43],"method,":[44],"but":[45],"a":[48,54,134,142],"high":[49,96,143],"complication":[50],"rate":[51],"poses":[53],"huge":[55],"economic":[56],"burden":[57],"patients.":[59],"The":[60],"gradient":[61],"boosted":[62],"decision":[63],"trees":[64],"(GBDT)":[65],"model":[66,169],"proposed":[67],"in":[68],"this":[69],"paper":[70],"is":[71,91],"used":[72],"predict":[74],"probability":[76,156,174],"risk":[78,147,176,190],"factors":[79,148,177,191],"that":[80,149,170],"AAA,":[83,180],"prediction":[86],"accuracy":[87],"algorithm":[90],"able":[92],"reach":[94],"96%.":[98],"This":[99,164],"study":[100,165],"selected":[101],"15":[102],"related":[103],"features":[105],"training":[107],"samples.":[108],"After":[109],"training,":[111],"age,":[112],"triglycerides":[113],"(TG),":[114],"blood":[115,122],"pressure":[116],"(BP),":[117],"low-density":[118],"lipoprotein":[119],"cholesterol":[120],"(LDL-C),":[121],"glucose":[123],"(Glu),":[124],"body":[126],"mass":[127],"index":[128],"(BMI)":[129],"are":[130],"found":[131],"have":[133],"direct":[135],"impact":[136],"AAA.":[138,195],"For":[139],"individuals":[140],"with":[141,160],"probability,":[145],"contribute":[150],"most":[152],"can":[157],"be":[158],"determined":[159],"GBDT":[162,168],"model.":[163],"presents":[166],"effectively":[171],"predicts":[172],"early":[179,184],"which":[181],"enables":[182],"an":[183],"intervention":[185],"control":[187],"these":[189],"against":[192],"incidence":[193]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
