{"id":"https://openalex.org/W7150856009","doi":"https://doi.org/10.58346/jowua.2026.i1.052","title":"Adaptive CNN-Based Multi-Comorbid Heart Disease Prediction Using US Dataset with Clinically Validated Feature Optimization","display_name":"Adaptive CNN-Based Multi-Comorbid Heart Disease Prediction Using US Dataset with Clinically Validated Feature Optimization","publication_year":2026,"publication_date":"2026-03-31","ids":{"openalex":"https://openalex.org/W7150856009","doi":"https://doi.org/10.58346/jowua.2026.i1.052"},"language":"en","primary_location":{"id":"doi:10.58346/jowua.2026.i1.052","doi":"https://doi.org/10.58346/jowua.2026.i1.052","is_oa":true,"landing_page_url":"https://doi.org/10.58346/jowua.2026.i1.052","pdf_url":"https://doi.org/10.58346/jowua.2026.i1.052","source":{"id":"https://openalex.org/S2738080829","display_name":"Journal of Wireless Mobile Networks Ubiquitous Computing and Dependable Applications","issn_l":"2093-5374","issn":["2093-5374","2093-5382"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","poland-70","russia-white-list-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://doi.org/10.58346/jowua.2026.i1.052","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139545914","display_name":"Nazia Sultana","orcid":"https://orcid.org/0009-0008-5104-6793","observed_orcids":["https://orcid.org/0009-0008-5104-6793"]},"institutions":[{"id":"https://openalex.org/I65674248","display_name":"Visvesvaraya Technological University","ror":"https://ror.org/00ha14p11","country_code":"IN","type":"education","lineage":["https://openalex.org/I65674248"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Nazia Sultana","raw_affiliation_strings":["Research scholar, Department of Computer Science and Engineering , Visvesvaraya Technological University , Mysuru , Karnataka , India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Research scholar, Department of Computer Science and Engineering , Visvesvaraya Technological University , Mysuru , Karnataka , India","institution_ids":["https://openalex.org/I65674248"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5133033277","display_name":"Dr.P.K. Kumar","orcid":"https://orcid.org/0000-0003-2786-7889","observed_orcids":["https://orcid.org/0000-0003-2786-7889"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dr.P.K. Kumar","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"17","issue":"1","first_page":"950","last_page":"969"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.9994999766349792,"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"}},"topics":[{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.9994999766349792,"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":9.999999747378752e-05,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":9.999999747378752e-05,"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/adaptive-convolutional-neural-network","display_name":"adaptive convolutional neural network","score":0.8610000014305115},{"id":"https://openalex.org/keywords/hypertension","display_name":"hypertension","score":0.625},{"id":"https://openalex.org/keywords/diabetes-mellitus","display_name":"diabetes mellitus","score":0.9909999966621399},{"id":"https://openalex.org/keywords/cardiac-arrhythmia","display_name":"cardiac arrhythmias","score":0.9980000257492065},{"id":"https://openalex.org/keywords/obesity","display_name":"obesity","score":0.9990000128746033},{"id":"https://openalex.org/keywords/shap","display_name":"SHAP","score":0.8489999771118164},{"id":"https://openalex.org/keywords/cardiovascular-risk-assessment","display_name":"cardiovascular risk assessment","score":0.7409999966621399},{"id":"https://openalex.org/keywords/clinical-decision-support","display_name":"clinical decision support","score":0.984000027179718},{"id":"https://openalex.org/keywords/model-generalization","display_name":"model generalization","score":0.5099999904632568}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.8069000244140625},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6478999853134155},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6241000294685364},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5273000001907349},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4377000033855438},{"id":"https://openalex.org/C2780074459","wikidata":"https://www.wikidata.org/wiki/Q389735","display_name":"Heart disease","level":2,"score":0.41429999470710754},{"id":"https://openalex.org/C534262118","wikidata":"https://www.wikidata.org/wiki/Q177719","display_name":"Medical diagnosis","level":2,"score":0.4075999855995178},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.40380001068115234},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4016000032424927},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3953000009059906},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36820000410079956},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.3628000020980835},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.3517000079154968},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.3296999931335449},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.31859999895095825},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.3118000030517578},{"id":"https://openalex.org/C27158222","wikidata":"https://www.wikidata.org/wiki/Q5532422","display_name":"Generalizability theory","level":2,"score":0.3089999854564667},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.30660000443458557},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.29179999232292175},{"id":"https://openalex.org/C2779159551","wikidata":"https://www.wikidata.org/wiki/Q1414874","display_name":"Comorbidity","level":2,"score":0.29030001163482666},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.2793000042438507},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.2727000117301941},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.27239999175071716}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.58346/jowua.2026.i1.052","doi":"https://doi.org/10.58346/jowua.2026.i1.052","is_oa":true,"landing_page_url":"https://doi.org/10.58346/jowua.2026.i1.052","pdf_url":"https://doi.org/10.58346/jowua.2026.i1.052","source":{"id":"https://openalex.org/S2738080829","display_name":"Journal of Wireless Mobile Networks Ubiquitous Computing and Dependable Applications","issn_l":"2093-5374","issn":["2093-5374","2093-5382"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","poland-70","russia-white-list-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.58346/jowua.2026.i1.052","doi":"https://doi.org/10.58346/jowua.2026.i1.052","is_oa":true,"landing_page_url":"https://doi.org/10.58346/jowua.2026.i1.052","pdf_url":"https://doi.org/10.58346/jowua.2026.i1.052","source":{"id":"https://openalex.org/S2738080829","display_name":"Journal of Wireless Mobile Networks Ubiquitous Computing and Dependable Applications","issn_l":"2093-5374","issn":["2093-5374","2093-5382"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","poland-70","russia-white-list-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.9537000060081482,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7150856009.pdf","grobid_xml":"https://content.openalex.org/works/W7150856009.grobid-xml"},"referenced_works_count":21,"referenced_works":["https://openalex.org/W3185516026","https://openalex.org/W3208423608","https://openalex.org/W4281656136","https://openalex.org/W4283216554","https://openalex.org/W4313398059","https://openalex.org/W4321372210","https://openalex.org/W4327938303","https://openalex.org/W4385707420","https://openalex.org/W4388775240","https://openalex.org/W4389207157","https://openalex.org/W4391821278","https://openalex.org/W4399020286","https://openalex.org/W4401442856","https://openalex.org/W4403202439","https://openalex.org/W4403330305","https://openalex.org/W4407768433","https://openalex.org/W4413147921","https://openalex.org/W4413743014","https://openalex.org/W4414689006","https://openalex.org/W4415646753","https://openalex.org/W4416538672"],"related_works":["https://openalex.org/W7127576887","https://openalex.org/W7129497818","https://openalex.org/W7103994666","https://openalex.org/W7164006643","https://openalex.org/W7213316914","https://openalex.org/W7217640885","https://openalex.org/W7117989810","https://openalex.org/W7216992716","https://openalex.org/W4408365815","https://openalex.org/W7164553933"],"abstract_inverted_index":{"The":[0,202],"prediction":[1,16],"of":[2,63,66,74,103,113,149,182,187,213,224],"heart":[3,48],"disease":[4,49],"with":[5,173],"several":[6],"comorbid":[7],"conditions":[8,23],"(hypertension,":[9],"diabetes,":[10],"arrhythmia,":[11],"and":[12,24,70,93,121,136,157,184,189,192,209,231],"obesity)":[13],"makes":[14],"the":[15,75,124,222],"complex":[17],"due":[18],"to":[19,31,45,100,141,219],"nonlinear":[20],"interactions":[21],"between":[22],"heterogeneous":[25],"risk":[26,217],"patterns.":[27],"As":[28],"a":[29,51,64,106,146,198],"solution":[30],"these":[32],"research":[33],"issues,":[34],"this":[35],"paper":[36],"suggests":[37],"an":[38,180,185],"Adaptive":[39],"Convolutional":[40],"Neural":[41],"Network":[42],"(CNN)":[43],"architecture":[44],"predict":[46],"multi-comorbid":[47,215],"on":[50,87,170],"unified":[52],"U.S.":[53],"Heart":[54],"Disease":[55],"dataset":[56],"(n":[57],"=":[58],"1,025)":[59],"that":[60,110,115,194],"is":[61,167,195],"comprised":[62],"union":[65],"Cleveland,":[67],"Hungarian,":[68],"Switzerland,":[69],"VA":[71],"repositories.":[72],"Out":[73],"76":[76],"attributes,":[77],"24":[78],"clinically":[79,210],"validated":[80],"features":[81],"were":[82,98],"then":[83],"picked":[84],"up":[85],"based":[86],"correlation":[88],"ranking,":[89],"SHAP-based":[90,152],"interpretability":[91],"analysis,":[92],"cardiologist":[94],"validation.":[95],"These":[96],"characteristics":[97],"converted":[99],"structured":[101],"representations":[102],"images":[104],"through":[105,131],"feature-to-image":[107],"encoding":[108],"plan":[109],"facilitated":[111],"learning":[112],"patterns":[114],"are":[116,139],"deep":[117],"both":[118],"in":[119,145,197,221],"space":[120],"relationally.":[122],"In":[123],"proposed":[125,203],"CNN":[126],"architecture,":[127],"layer-driven":[128],"optimization,":[129],"regularization":[130],"dropout,":[132],"adaptive":[133],"learning-rate":[134],"scheduling,":[135],"reproducibility":[137],"control":[138],"included":[140],"guarantee":[142],"consistent":[143],"generalization":[144],"wide":[147],"range":[148],"comorbidity":[150],"groups.":[151],"feature":[153],"attribution":[154],"generates":[155],"clinical":[156,164,199,232],"understandable":[158],"explanations":[159],"about":[160],"model":[161],"predictions,":[162],"achieving":[163],"interpretability.":[165],"There":[166],"strong":[168],"performance":[169],"experimental":[171],"evaluation":[172],"92%":[174],"accuracy,":[175],"89%":[176],"precision,":[177],"90%":[178],"recall,":[179],"F1-score":[181],"0.89,":[183],"ROC-AUC":[186],"0.94,":[188],"high":[190],"sensitivity":[191],"specificity":[193],"applicable":[196],"decision-support":[200],"setting.":[201],"framework":[204],"can":[205],"provide":[206],"scalable,":[207],"interpretable,":[208],"reviewed":[211],"methods":[212],"automated":[214],"cardiac":[216],"assessment":[218],"aid":[220],"integration":[223],"AI":[225],"transparency":[226],"into":[227],"healthcare":[228],"system":[229],"components":[230],"implementation.":[233]},"counts_by_year":[],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2026-04-07T00:00:00"}
