{"id":"https://openalex.org/W4393023621","doi":"https://doi.org/10.48550/arxiv.2403.11032","title":"FH-TabNet: Multi-Class Familial Hypercholesterolemia Detection via a Multi-Stage Tabular Deep Learning","display_name":"FH-TabNet: Multi-Class Familial Hypercholesterolemia Detection via a Multi-Stage Tabular Deep Learning","publication_year":2024,"publication_date":"2024-03-16","ids":{"openalex":"https://openalex.org/W4393023621","doi":"https://doi.org/10.48550/arxiv.2403.11032"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2403.11032","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2403.11032","pdf_url":"https://arxiv.org/pdf/2403.11032","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2403.11032","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5071473768","display_name":"Sadaf Khademi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Khademi, Sadaf","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5092246629","display_name":"Zohreh Hajiakhondi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hajiakhondi, Zohreh","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008193868","display_name":"Golnaz Vaseghi","orcid":"https://orcid.org/0000-0003-3040-6135"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vaseghi, Golnaz","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102822921","display_name":"Nizal Sarrafzadegan","orcid":"https://orcid.org/0000-0002-6828-2169"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sarrafzadegan, Nizal","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5058253407","display_name":"Arash Mohammadi","orcid":"https://orcid.org/0000-0003-1972-7923"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mohammadi, Arash","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"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":0.8618999719619751,"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":0.8618999719619751,"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":0.8560000061988831,"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"}},{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.8371000289916992,"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/class","display_name":"Class (philosophy)","score":0.6522247791290283},{"id":"https://openalex.org/keywords/familial-hypercholesterolemia","display_name":"Familial hypercholesterolemia","score":0.6438671946525574},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5102787017822266},{"id":"https://openalex.org/keywords/stage","display_name":"Stage (stratigraphy)","score":0.4775320291519165},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4673937261104584},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.42360520362854004},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.23145422339439392},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.21902969479560852},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.1764880120754242},{"id":"https://openalex.org/keywords/cholesterol","display_name":"Cholesterol","score":0.0899491012096405}],"concepts":[{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.6522247791290283},{"id":"https://openalex.org/C2779120738","wikidata":"https://www.wikidata.org/wiki/Q2711291","display_name":"Familial hypercholesterolemia","level":3,"score":0.6438671946525574},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5102787017822266},{"id":"https://openalex.org/C146357865","wikidata":"https://www.wikidata.org/wiki/Q1123245","display_name":"Stage (stratigraphy)","level":2,"score":0.4775320291519165},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4673937261104584},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.42360520362854004},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.23145422339439392},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.21902969479560852},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.1764880120754242},{"id":"https://openalex.org/C2778163477","wikidata":"https://www.wikidata.org/wiki/Q43656","display_name":"Cholesterol","level":2,"score":0.0899491012096405},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2403.11032","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2403.11032","pdf_url":"https://arxiv.org/pdf/2403.11032","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2403.11032","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2403.11032","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2403.11032","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2403.11032","pdf_url":"https://arxiv.org/pdf/2403.11032","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320334593","display_name":"Natural Sciences and Engineering Research Council of Canada","ror":"https://ror.org/01h531d29"}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4393023621.pdf"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2731899572","https://openalex.org/W3215138031","https://openalex.org/W3009238340","https://openalex.org/W4321369474","https://openalex.org/W4360585206","https://openalex.org/W4285208911","https://openalex.org/W3082895349","https://openalex.org/W4213079790","https://openalex.org/W2248239756","https://openalex.org/W4323565446"],"abstract_inverted_index":{"Familial":[0],"Hypercholesterolemia":[1],"(FH)":[2],"is":[3,26,44,104,128,180],"a":[4,48,65,84,129,148],"genetic":[5],"disorder":[6],"characterized":[7],"by":[8,123],"elevated":[9],"levels":[10],"of":[11,24,27,37,68,97,113],"Low-Density":[12],"Lipoprotein":[13],"(LDL)":[14],"cholesterol":[15],"or":[16],"its":[17,94,106],"associated":[18],"genes.":[19],"Early-stage":[20],"and":[21,47,139,161],"accurate":[22],"categorization":[23,157],"FH":[25,78,102,141,190],"significance":[28],"allowing":[29],"for":[30,53,76,101,134,155],"timely":[31],"interventions":[32],"to":[33,110,171],"mitigate":[34],"the":[35,114,125,194],"risk":[36],"life-threatening":[38],"conditions.":[39],"Conventional":[40],"diagnosis":[41],"approach,":[42],"however,":[43],"complex,":[45],"costly,":[46],"challenging":[49,195],"interpretation":[50],"task":[51,87],"even":[52],"experienced":[54],"clinicians":[55],"resulting":[56],"in":[57,70,105,188,193],"high":[58],"underdiagnosis":[59],"rates.":[60],"Although":[61],"there":[62],"has":[63],"been":[64],"recent":[66],"surge":[67],"interest":[69],"using":[71,89],"Machine":[72],"Learning":[73,99],"(ML)":[74],"models":[75],"early":[77],"detection,":[79],"existing":[80],"solutions":[81],"only":[82],"consider":[83],"binary":[85],"classification":[86],"solely":[88],"classical":[90],"ML":[91],"models.":[92],"Despite":[93],"significance,":[95],"application":[96],"Deep":[98],"(DL)":[100],"detection":[103],"infancy,":[107],"possibly,":[108],"due":[109],"categorical":[111],"nature":[112],"underlying":[115],"clinical":[116],"data.":[117],"The":[118,143,177],"paper":[119],"addresses":[120],"this":[121],"gap":[122],"introducing":[124],"FH-TabNet,":[126],"which":[127],"multi-stage":[130],"tabular":[131,150],"DL":[132],"network":[133],"multi-class":[135],"(Definite,":[136],"Probable,":[137],"Possible,":[138],"Unlikely)":[140],"detection.":[142],"FH-TabNet":[144],"initially":[145],"involves":[146],"applying":[147],"deep":[149],"data":[151],"learning":[152],"architecture":[153],"(TabNet)":[154],"primary":[156],"into":[158],"healthy":[159],"(Possible/Unlikely)":[160],"patient":[162],"(Probable/Definite)":[163],"classes.":[164],"Subsequently,":[165],"independent":[166],"TabNet":[167],"classifiers":[168],"are":[169],"applied":[170],"each":[172],"subgroup,":[173],"enabling":[174],"refined":[175],"classification.":[176],"model's":[178],"performance":[179,187],"evaluated":[181],"through":[182],"5-fold":[183],"cross-validation":[184],"illustrating":[185],"superior":[186],"categorizing":[189],"patients,":[191],"particularly":[192],"low-prevalence":[196],"subcategories.":[197]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2024-03-21T00:00:00"}
