{"id":"https://openalex.org/W4416962104","doi":"https://doi.org/10.1109/ist66504.2025.11268424","title":"Machine Learning Diagnostic Model for Ischemic Heart Disease Based on Multi-Domain Magnetocardiography","display_name":"Machine Learning Diagnostic Model for Ischemic Heart Disease Based on Multi-Domain Magnetocardiography","publication_year":2025,"publication_date":"2025-10-15","ids":{"openalex":"https://openalex.org/W4416962104","doi":"https://doi.org/10.1109/ist66504.2025.11268424"},"language":null,"primary_location":{"id":"doi:10.1109/ist66504.2025.11268424","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ist66504.2025.11268424","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Imaging Systems and Techniques (IST)","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/A5066225387","display_name":"Junting Li","orcid":"https://orcid.org/0000-0001-5840-1470"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Junting Li","raw_affiliation_strings":["Beihang University Beijing,School of Instrumentation Science and Optoelectronic Engineering,China"],"affiliations":[{"raw_affiliation_string":"Beihang University Beijing,School of Instrumentation Science and Optoelectronic Engineering,China","institution_ids":["https://openalex.org/I82880672"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5066225387"],"corresponding_institution_ids":["https://openalex.org/I82880672"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.32188521,"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":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11993","display_name":"Atomic and Subatomic Physics Research","score":0.6330000162124634,"subfield":{"id":"https://openalex.org/subfields/3107","display_name":"Atomic and Molecular Physics, and Optics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11993","display_name":"Atomic and Subatomic Physics Research","score":0.6330000162124634,"subfield":{"id":"https://openalex.org/subfields/3107","display_name":"Atomic and Molecular Physics, and Optics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10378","display_name":"Advanced MRI Techniques and Applications","score":0.16740000247955322,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"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/T10372","display_name":"Cardiac Imaging and Diagnostics","score":0.08420000225305557,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"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/magnetocardiography","display_name":"Magnetocardiography","score":0.7440999746322632},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.6868000030517578},{"id":"https://openalex.org/keywords/adaboost","display_name":"AdaBoost","score":0.6736000180244446},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5236999988555908},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5223000049591064},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.47540000081062317},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.45840001106262207},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41819998621940613},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.39660000801086426}],"concepts":[{"id":"https://openalex.org/C2778475581","wikidata":"https://www.wikidata.org/wiki/Q1884389","display_name":"Magnetocardiography","level":2,"score":0.7440999746322632},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.711899995803833},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6868000030517578},{"id":"https://openalex.org/C141404830","wikidata":"https://www.wikidata.org/wiki/Q2823869","display_name":"AdaBoost","level":3,"score":0.6736000180244446},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6191999912261963},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5236999988555908},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5223000049591064},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.47540000081062317},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.45840001106262207},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.44940000772476196},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41819998621940613},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.39660000801086426},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.39259999990463257},{"id":"https://openalex.org/C199163554","wikidata":"https://www.wikidata.org/wiki/Q1681619","display_name":"Univariate","level":3,"score":0.3682999908924103},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.36230000853538513},{"id":"https://openalex.org/C2780074459","wikidata":"https://www.wikidata.org/wiki/Q389735","display_name":"Heart disease","level":2,"score":0.3476000130176544},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.3425000011920929},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.3181999921798706},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.31520000100135803},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.31450000405311584},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.3057999908924103},{"id":"https://openalex.org/C2779134260","wikidata":"https://www.wikidata.org/wiki/Q12136","display_name":"Disease","level":2,"score":0.2973000109195709},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.2962000072002411},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2822999954223633},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.2694999873638153},{"id":"https://openalex.org/C3020132585","wikidata":"https://www.wikidata.org/wiki/Q2671652","display_name":"Diagnostic accuracy","level":2,"score":0.2685000002384186},{"id":"https://openalex.org/C143409427","wikidata":"https://www.wikidata.org/wiki/Q161238","display_name":"Magnetic resonance imaging","level":2,"score":0.26649999618530273},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2597000002861023}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ist66504.2025.11268424","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ist66504.2025.11268424","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Imaging Systems and Techniques (IST)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1260204519","https://openalex.org/W1484080302","https://openalex.org/W1936833235","https://openalex.org/W1973102663","https://openalex.org/W1973952028","https://openalex.org/W2018504033","https://openalex.org/W2042947293","https://openalex.org/W2064654820","https://openalex.org/W2105397247","https://openalex.org/W2112502087","https://openalex.org/W2115350250","https://openalex.org/W2166799166","https://openalex.org/W2898314458","https://openalex.org/W3113216408","https://openalex.org/W3160031055","https://openalex.org/W4389542143","https://openalex.org/W4389574595","https://openalex.org/W4393858312","https://openalex.org/W4409172695"],"related_works":[],"abstract_inverted_index":{"This":[0],"study":[1],"aims":[2],"to":[3,75,210,223],"develop":[4],"an":[5,162,180],"intelligent":[6],"diagnostic":[7,221,247],"system":[8,241],"for":[9,116,150,249],"ischemic":[10],"heart":[11],"disease":[12],"(IHD)":[13],"by":[14,190],"integrating":[15],"Spin":[16],"Exchange":[17],"Relaxation":[18],"Free":[19],"magnetocardiography":[20],"(SERF-MCG)":[21],"with":[22,161,179,216],"advanced":[23],"machine":[24,124],"learning":[25,125],"techniques.":[26],"We":[27,118],"analyzed":[28],"cardiac":[29,81],"magnetic":[30,82],"signals":[31],"from":[32,59,204],"565":[33],"patients":[34],"(336":[35],"cases":[36],"clinically":[37],"diagnosed":[38],"as":[39],"IHD":[40,211,258],"and":[41,51,62,71,91,102,143,174,198,219,225,232,245,260],"229":[42],"non-IHD":[43],"cases).":[44],"After":[45],"data":[46,52],"preprocessing":[47],"including":[48,127],"noise":[49],"suppression":[50],"format":[53],"conversion,":[54],"2,317":[55],"features":[56,113],"were":[57,114],"extracted":[58],"ST":[60],"segments":[61],"T-waves":[63],"across":[64],"time":[65],"domain,":[66,68],"frequency":[67],"isomagnetic":[69,205],"maps,":[70],"current":[72],"density":[73],"maps":[74],"ensure":[76],"the":[77,110,120,151,158,191,230,252],"comprehensive":[78],"mining":[79],"of":[80,109,122,153,164,167,171,176,184,196,201,234,254],"signals.":[83],"Feature":[84],"screening":[85],"was":[86],"conducted":[87],"using":[88],"univariate":[89],"tests":[90],"four":[92],"predictors:":[93],"LASSO":[94],"regression,":[95],"random":[96,155],"forest,":[97],"Minimum":[98],"Redundancy,":[99],"Maximum":[100],"relevance,":[101],"Light":[103],"Gradient":[104],"Boosting":[105],"machine.":[106],"Ultimately,":[107],"18":[108],"most":[111],"distinctive":[112],"retained":[115],"modeling.":[117],"compared":[119],"performance":[121,160],"different":[123],"classifiers,":[126],"Logistic":[128],"Regression,":[129],"Support":[130],"Vector":[131],"Machine,":[132],"K-Nearest":[133],"Neighbor,":[134],"Naive":[135],"Bayes,":[136],"Decision":[137],"Tree,":[138],"Random":[139],"Forest,":[140],"XGBoost,":[141],"AdaBoost":[142],"Neural":[144],"Network.":[145],"The":[146,187,239],"results":[147],"showed":[148],"that":[149],"detection":[152],"IHD,":[154],"forest":[156],"achieved":[157],"best":[159],"AUC":[163],"0.87,":[165],"sensitivity":[166],"$91":[168],"\\%$,":[169,178],"specificity":[170],"$74":[172],"\\%$":[173],"accuracy":[175],"$84":[177],"$F":[181],"1$":[182],"score":[183],"$87":[185],"\\%$.":[186],"model":[188],"interpretation":[189],"SHAP":[192],"algorithm":[193],"indicated":[194],"roundness":[195],"positive":[197],"negative":[199],"poles":[200],"T-wave":[202],"peaks":[203],"map":[206],"made":[207],"major":[208],"contributions":[209],"classification.":[212],"It":[213],"provides":[214,242],"clinicians":[215],"a":[217,243],"rapid":[218,244],"accurate":[220,246],"tool":[222,248],"process":[224],"interpret":[226],"MCG":[227,235],"data,":[228],"enhancing":[229,261],"acceptance":[231],"applicability":[233],"in":[236,256],"clinical":[237,262],"practice.":[238],"proposed":[240],"clinicians,":[250],"demonstrating":[251],"potential":[253],"SERF-MCG":[255],"optimizing":[257],"diagnosis":[259],"applicability.":[263]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-12-03T00:00:00"}
