{"id":"https://openalex.org/W7160423962","doi":"https://doi.org/10.1007/s11222-026-10877-x","title":"A model-based feature selection approach for type 2 Diabetes Mellitus diagnosis using Heart Rate and Systolic Arterial Pressure series measures","display_name":"A model-based feature selection approach for type 2 Diabetes Mellitus diagnosis using Heart Rate and Systolic Arterial Pressure series measures","publication_year":2026,"publication_date":"2026-05-06","ids":{"openalex":"https://openalex.org/W7160423962","doi":"https://doi.org/10.1007/s11222-026-10877-x"},"language":"en","primary_location":{"id":"doi:10.1007/s11222-026-10877-x","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11222-026-10877-x","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11222-026-10877-x.pdf","source":{"id":"https://openalex.org/S5437875","display_name":"Statistics and Computing","issn_l":"0960-3174","issn":["0960-3174","1573-1375"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Statistics and Computing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s11222-026-10877-x.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130950897","display_name":"Javier Roca-Pardinas","orcid":null},"institutions":[{"id":"https://openalex.org/I6289922","display_name":"Universidade de Vigo","ror":"https://ror.org/05rdf8595","country_code":"ES","type":"education","lineage":["https://openalex.org/I6289922"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Javier Roca-Pardi\u00f1as","raw_affiliation_strings":["Departamento de Estad\u00edstica e Investigaci\u00f3n Operativa, Universidade de Vigo, Campus As Lagoas - Marcosende, 36310, Vigo, Spain"],"raw_orcid":"https://orcid.org/0000-0003-3107-4515","affiliations":[{"raw_affiliation_string":"Departamento de Estad\u00edstica e Investigaci\u00f3n Operativa, Universidade de Vigo, Campus As Lagoas - Marcosende, 36310, Vigo, Spain","institution_ids":["https://openalex.org/I6289922"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135508417","display_name":"Mar\u00eda J. Lado","orcid":"https://orcid.org/0000-0002-9027-7788"},"institutions":[{"id":"https://openalex.org/I6289922","display_name":"Universidade de Vigo","ror":"https://ror.org/05rdf8595","country_code":"ES","type":"education","lineage":["https://openalex.org/I6289922"]}],"countries":["ES"],"is_corresponding":true,"raw_author_name":"Mar\u00eda J. Lado","raw_affiliation_strings":["Escola Superior de Enxe\u00f1ar\u00eda Inform\u00e1tica, IFCAE - Universidade de Vigo, Campus As Lagoas, 32004, Ourense, Spain"],"raw_orcid":"https://orcid.org/0000-0002-9027-7788","affiliations":[{"raw_affiliation_string":"Escola Superior de Enxe\u00f1ar\u00eda Inform\u00e1tica, IFCAE - Universidade de Vigo, Campus As Lagoas, 32004, Ourense, Spain","institution_ids":["https://openalex.org/I6289922"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5135466385","display_name":"Leandro Rodr\u00edguez-Li\u00f1ares","orcid":"https://orcid.org/0000-0002-7087-899X"},"institutions":[{"id":"https://openalex.org/I6289922","display_name":"Universidade de Vigo","ror":"https://ror.org/05rdf8595","country_code":"ES","type":"education","lineage":["https://openalex.org/I6289922"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Leandro Rodr\u00edguez-Li\u00f1ares","raw_affiliation_strings":["Escola Superior de Enxe\u00f1ar\u00eda Inform\u00e1tica, IFCAE - Universidade de Vigo, Campus As Lagoas, 32004, Ourense, Spain"],"raw_orcid":"https://orcid.org/0000-0002-7087-899X","affiliations":[{"raw_affiliation_string":"Escola Superior de Enxe\u00f1ar\u00eda Inform\u00e1tica, IFCAE - Universidade de Vigo, Campus As Lagoas, 32004, Ourense, Spain","institution_ids":["https://openalex.org/I6289922"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5135508417"],"corresponding_institution_ids":["https://openalex.org/I6289922"],"apc_list":{"value":2090,"currency":"EUR","value_usd":2690},"apc_paid":{"value":2090,"currency":"EUR","value_usd":2690},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.69782934,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"36","issue":"3","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10745","display_name":"Heart Rate Variability and Autonomic Control","score":0.8003000020980835,"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"}},"topics":[{"id":"https://openalex.org/T10745","display_name":"Heart Rate Variability and Autonomic Control","score":0.8003000020980835,"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/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.05249999836087227,"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/T11021","display_name":"ECG Monitoring and Analysis","score":0.03060000017285347,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.6859999895095825},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.6642000079154968},{"id":"https://openalex.org/keywords/receiver-operating-characteristic","display_name":"Receiver operating characteristic","score":0.578000009059906},{"id":"https://openalex.org/keywords/type-2-diabetes-mellitus","display_name":"Type 2 Diabetes Mellitus","score":0.5375999808311462},{"id":"https://openalex.org/keywords/blood-pressure","display_name":"Blood pressure","score":0.47290000319480896},{"id":"https://openalex.org/keywords/sensitivity","display_name":"Sensitivity (control systems)","score":0.45899999141693115},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4196000099182129},{"id":"https://openalex.org/keywords/baroreflex","display_name":"Baroreflex","score":0.41589999198913574},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.40610000491142273},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.40310001373291016}],"concepts":[{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.6859999895095825},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.6642000079154968},{"id":"https://openalex.org/C58471807","wikidata":"https://www.wikidata.org/wiki/Q327120","display_name":"Receiver operating characteristic","level":2,"score":0.578000009059906},{"id":"https://openalex.org/C2910068830","wikidata":"https://www.wikidata.org/wiki/Q3025883","display_name":"Type 2 Diabetes Mellitus","level":3,"score":0.5375999808311462},{"id":"https://openalex.org/C84393581","wikidata":"https://www.wikidata.org/wiki/Q82642","display_name":"Blood pressure","level":2,"score":0.47290000319480896},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.45899999141693115},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44269999861717224},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4196000099182129},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.41620001196861267},{"id":"https://openalex.org/C191237925","wikidata":"https://www.wikidata.org/wiki/Q808725","display_name":"Baroreflex","level":4,"score":0.41589999198913574},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.40610000491142273},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.40310001373291016},{"id":"https://openalex.org/C164705383","wikidata":"https://www.wikidata.org/wiki/Q10379","display_name":"Cardiology","level":1,"score":0.4002000093460083},{"id":"https://openalex.org/C2777953023","wikidata":"https://www.wikidata.org/wiki/Q1073121","display_name":"Heart rate","level":3,"score":0.399399995803833},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.3880999982357025},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.3772999942302704},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3756999969482422},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.3582000136375427},{"id":"https://openalex.org/C555293320","wikidata":"https://www.wikidata.org/wiki/Q12206","display_name":"Diabetes mellitus","level":2,"score":0.3546000123023987},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.319599986076355},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.31850001215934753},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.3156999945640564},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.298799991607666},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.2987000048160553},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.289000004529953},{"id":"https://openalex.org/C2777180221","wikidata":"https://www.wikidata.org/wiki/Q3025883","display_name":"Type 2 diabetes","level":3,"score":0.2874999940395355},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.28529998660087585},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.27950000762939453},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.27649998664855957},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.27090001106262207},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.2662999927997589},{"id":"https://openalex.org/C95922358","wikidata":"https://www.wikidata.org/wiki/Q5432725","display_name":"False positive rate","level":2,"score":0.2660999894142151},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.2632000148296356},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2572000026702881},{"id":"https://openalex.org/C71635504","wikidata":"https://www.wikidata.org/wiki/Q933954","display_name":"Heart rate variability","level":4,"score":0.25429999828338623}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1007/s11222-026-10877-x","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11222-026-10877-x","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11222-026-10877-x.pdf","source":{"id":"https://openalex.org/S5437875","display_name":"Statistics and Computing","issn_l":"0960-3174","issn":["0960-3174","1573-1375"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Statistics and Computing","raw_type":"journal-article"},{"id":"pmh:oai:www.investigo.biblioteca.uvigo.es:11093/12040","is_oa":true,"landing_page_url":"http://hdl.handle.net/11093/12040","pdf_url":null,"source":{"id":"https://openalex.org/S4377196408","display_name":"Investigo Institutional repository of UVigo (Universidade de Vigo)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I6289922","host_organization_name":"Universidade de Vigo","host_organization_lineage":["https://openalex.org/I6289922"],"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":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.1007/s11222-026-10877-x","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11222-026-10877-x","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11222-026-10877-x.pdf","source":{"id":"https://openalex.org/S5437875","display_name":"Statistics and Computing","issn_l":"0960-3174","issn":["0960-3174","1573-1375"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Statistics and Computing","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.6982821822166443}],"awards":[],"funders":[{"id":"https://openalex.org/F4320324091","display_name":"Universidade de Vigo","ror":"https://ror.org/05rdf8595"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7160423962.pdf","grobid_xml":"https://content.openalex.org/works/W7160423962.grobid-xml"},"referenced_works_count":65,"referenced_works":["https://openalex.org/W96485157","https://openalex.org/W325781386","https://openalex.org/W1520722002","https://openalex.org/W1988381397","https://openalex.org/W1997560427","https://openalex.org/W2000056219","https://openalex.org/W2032300448","https://openalex.org/W2032485049","https://openalex.org/W2038216402","https://openalex.org/W2039897810","https://openalex.org/W2044309407","https://openalex.org/W2053160176","https://openalex.org/W2053924421","https://openalex.org/W2054652204","https://openalex.org/W2058815839","https://openalex.org/W2076349866","https://openalex.org/W2092979919","https://openalex.org/W2100596015","https://openalex.org/W2118765312","https://openalex.org/W2119160928","https://openalex.org/W2124146263","https://openalex.org/W2133795831","https://openalex.org/W2134293572","https://openalex.org/W2135046866","https://openalex.org/W2138969187","https://openalex.org/W2140104458","https://openalex.org/W2140739356","https://openalex.org/W2145912168","https://openalex.org/W2158316475","https://openalex.org/W2161418134","https://openalex.org/W2168175751","https://openalex.org/W2171138447","https://openalex.org/W2185461071","https://openalex.org/W2191755155","https://openalex.org/W2285072859","https://openalex.org/W2298993849","https://openalex.org/W2342358887","https://openalex.org/W2588239017","https://openalex.org/W2791384939","https://openalex.org/W2895834328","https://openalex.org/W2896151579","https://openalex.org/W2965743638","https://openalex.org/W2970313615","https://openalex.org/W2983229458","https://openalex.org/W3034893840","https://openalex.org/W3094998422","https://openalex.org/W3095091111","https://openalex.org/W3112717819","https://openalex.org/W3173398390","https://openalex.org/W3181106170","https://openalex.org/W3192277055","https://openalex.org/W4206550329","https://openalex.org/W4280512154","https://openalex.org/W4292023222","https://openalex.org/W4295136441","https://openalex.org/W4297957988","https://openalex.org/W4328110101","https://openalex.org/W4383068644","https://openalex.org/W4384301529","https://openalex.org/W4388968678","https://openalex.org/W4390675381","https://openalex.org/W4396611353","https://openalex.org/W4407633792","https://openalex.org/W4408685263","https://openalex.org/W4411394465"],"related_works":[],"abstract_inverted_index":{"Abstract":[0],"Feature":[1],"selection":[2,63],"has":[3],"emerged":[4],"as":[5,141,143],"a":[6,39,60,107,184,189],"powerful":[7],"tool":[8],"for":[9],"evaluating":[10],"the":[11,77,83,86,96,168,198,206],"relevance":[12],"of":[13,42,98,186,193,200],"variables":[14,52],"in":[15],"classification":[16],"models.":[17],"In":[18,55],"high-dimensional":[19],"datasets,":[20],"not":[21],"all":[22],"features":[23,43,128],"contribute":[24],"meaningfully":[25],"to":[26,104,177,204],"accurate":[27],"classification,":[28],"and":[29,49,82,121,134,154,164,211],"their":[30],"inclusion":[31],"can":[32],"negatively":[33],"affect":[34],"model":[35,126,182],"performance.":[36],"Often,":[37],"only":[38],"small":[40],"subset":[41],"is":[44,53],"truly":[45],"informative;":[46],"therefore,":[47],"identifying":[48],"removing":[50],"irrelevant":[51],"crucial.":[54],"this":[56,99,201],"paper,":[57],"we":[58,101],"propose":[59],"novel":[61],"feature":[62],"method":[64],"based":[65],"on":[66],"logistic":[67],"Generalized":[68],"Additive":[69],"Models":[70],"(GAMs).":[71],"Model":[72],"performance":[73],"was":[74],"evaluated":[75],"using":[76],"Bayesian":[78],"Information":[79],"Criterion":[80],"(BIC)":[81],"Area":[84],"Under":[85],"ROC":[87],"-":[88],"Receiver":[89],"Operating":[90],"Characteristic-":[91],"Curve":[92],"(AUC).":[93],"To":[94],"demonstrate":[95],"effectiveness":[97],"approach,":[100],"applied":[102],"it":[103],"data":[105],"from":[106,130,175],"clinical":[108],"study":[109],"aimed":[110],"at":[111,152],"diagnosing":[112],"Type":[113],"2":[114],"Diabetes":[115],"Mellitus":[116],"(T2DM),":[117],"distinguishing":[118],"between":[119,162,208],"diabetic":[120],"healthy":[122],"individuals.":[123],"The":[124,158,179],"regression":[125],"incorporated":[127],"derived":[129],"Heart":[131],"Period":[132],"(HP)":[133],"Systolic":[135],"Arterial":[136],"Pressure":[137],"(SAP)":[138],"time":[139],"series,":[140],"well":[142],"Baroreflex":[144],"Sensitivity":[145],"(BRS)":[146],"measures,":[147],"collected":[148],"under":[149],"two":[150],"conditions:":[151],"rest":[153],"after":[155],"active":[156],"standing.":[157],"best-performing":[159],"models":[160],"selected":[161],"three":[163],"five":[165],"covariates.":[166],"Among":[167],"top":[169],"20":[170],"models,":[171],"AUC":[172],"values":[173],"ranged":[174],"0.931":[176],"0.918.":[178],"best":[180],"3-covariate":[181],"achieved":[183],"sensitivity":[185],"0.816":[187],"with":[188],"false":[190],"positive":[191],"rate":[192],"0.184.":[194],"These":[195],"results":[196],"highlight":[197],"potential":[199],"statistical":[202],"methodology":[203],"enhance":[205],"discrimination":[207],"T2DM":[209],"patients":[210],"control":[212],"subjects.":[213]},"counts_by_year":[],"updated_date":"2026-06-13T06:13:01.061226","created_date":"2026-05-07T00:00:00"}