{"id":"https://openalex.org/W7169588331","doi":"https://doi.org/10.1371/journal.pone.0353605","title":"A novel dynamic Optuna hybrid Harris Hawks Optimization approach for classification of CAD","display_name":"A novel dynamic Optuna hybrid Harris Hawks Optimization approach for classification of CAD","publication_year":2026,"publication_date":"2026-07-17","ids":{"openalex":"https://openalex.org/W7169588331","doi":"https://doi.org/10.1371/journal.pone.0353605","pmid":"https://pubmed.ncbi.nlm.nih.gov/42467701"},"language":"en","primary_location":{"id":"doi:10.1371/journal.pone.0353605","doi":"https://doi.org/10.1371/journal.pone.0353605","is_oa":true,"landing_page_url":"https://doi.org/10.1371/journal.pone.0353605","pdf_url":"https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0353605&type=printable","source":{"id":"https://openalex.org/S202381698","display_name":"PLoS ONE","issn_l":"1932-6203","issn":["1932-6203"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","dongbi-c","doyens","erih-plus","jufo-1","ki-jl-1","medline","norway-1","poland-100","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310315706","host_organization_name":"Public Library of Science","host_organization_lineage":["https://openalex.org/P4310315706"],"host_organization_lineage_names":["Public Library of Science"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"PLOS One","raw_type":"journal-article"},"volume":null,"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0353605&type=printable","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5116197812","display_name":"C. K. Revathi","orcid":"https://orcid.org/0009-0008-7807-6175","observed_orcids":["https://orcid.org/0009-0008-7807-6175"]},"institutions":[{"id":"https://openalex.org/I876193797","display_name":"Vellore Institute of Technology University","ror":"https://ror.org/00qzypv28","country_code":"IN","type":"education","lineage":["https://openalex.org/I876193797"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"C. K. Revathi","raw_affiliation_strings":["Full-time research scholar, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Full-time research scholar, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India","institution_ids":["https://openalex.org/I876193797"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053687969","display_name":"H. Santhi","orcid":"https://orcid.org/0000-0003-4664-4947","observed_orcids":["https://orcid.org/0000-0003-4664-4947"]},"institutions":[{"id":"https://openalex.org/I876193797","display_name":"Vellore Institute of Technology University","ror":"https://ror.org/00qzypv28","country_code":"IN","type":"education","lineage":["https://openalex.org/I876193797"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"H. Santhi","raw_affiliation_strings":["Associate Professor Senior, School of Computer Science and Engineering, Faculty of Database Management Systems, Vellore Institute of Technology, Vellore, Tamil Nadu, India"],"raw_orcid":"https://orcid.org/0000-0003-4664-4947","affiliations":[{"raw_affiliation_string":"Associate Professor Senior, School of Computer Science and Engineering, Faculty of Database Management Systems, Vellore Institute of Technology, Vellore, Tamil Nadu, India","institution_ids":["https://openalex.org/I876193797"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5053687969"],"corresponding_institution_ids":["https://openalex.org/I876193797"],"apc_list":{"value":2382,"currency":"USD","value_usd":2382},"apc_paid":{"value":2382,"currency":"USD","value_usd":2382},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.54129458,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"21","issue":"7","first_page":"e0353605","last_page":"e0353605"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10100","display_name":"Metaheuristic Optimization Algorithms Research","score":0.8730000257492065,"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/T10100","display_name":"Metaheuristic Optimization Algorithms Research","score":0.8730000257492065,"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":0.0957999974489212,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.007000000216066837,"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/coronary-artery-disease-prediction","display_name":"coronary artery disease prediction","score":0.9369999766349792},{"id":"https://openalex.org/keywords/coronary-stenosis","display_name":"coronary artery stenosis","score":0.9279999732971191},{"id":"https://openalex.org/keywords/harris-hawks-optimization","display_name":"Harris Hawks Optimization","score":0.8180000185966492},{"id":"https://openalex.org/keywords/optuna","display_name":"Optuna","score":0.8529999852180481},{"id":"https://openalex.org/keywords/hyperparameter-optimization","display_name":"hyperparameter optimization","score":0.843999981880188},{"id":"https://openalex.org/keywords/inception-v3","display_name":"InceptionV3","score":0.8209999799728394},{"id":"https://openalex.org/keywords/mobilenet","display_name":"MobileNet","score":0.9990000128746033},{"id":"https://openalex.org/keywords/resnet-50","display_name":"ResNet50","score":0.8759999871253967},{"id":"https://openalex.org/keywords/vgg16","display_name":"VGG16","score":0.9940000176429749},{"id":"https://openalex.org/keywords/dynamic-parameter-adjustment","display_name":"dynamic parameter adjustment","score":0.6779999732971191}],"concepts":[{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.6912000179290771},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6539999842643738},{"id":"https://openalex.org/C194789388","wikidata":"https://www.wikidata.org/wiki/Q17855283","display_name":"CAD","level":2,"score":0.6438000202178955},{"id":"https://openalex.org/C2778213512","wikidata":"https://www.wikidata.org/wiki/Q844935","display_name":"Coronary artery disease","level":2,"score":0.621399998664856},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.49059998989105225},{"id":"https://openalex.org/C10485038","wikidata":"https://www.wikidata.org/wiki/Q48996162","display_name":"Hyperparameter optimization","level":3,"score":0.48350000381469727},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45739999413490295},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.4309999942779541},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39959999918937683},{"id":"https://openalex.org/C58758708","wikidata":"https://www.wikidata.org/wiki/Q7240233","display_name":"Premature convergence","level":3,"score":0.3813000023365021},{"id":"https://openalex.org/C149672232","wikidata":"https://www.wikidata.org/wiki/Q337048","display_name":"Adaptive optimization","level":2,"score":0.3736000061035156},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.335999995470047},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.3257000148296356},{"id":"https://openalex.org/C164752517","wikidata":"https://www.wikidata.org/wiki/Q5570875","display_name":"Global optimization","level":2,"score":0.29350000619888306},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.27959999442100525},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.275299996137619},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.27320000529289246},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.26820001006126404},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2547000050544739},{"id":"https://openalex.org/C102248274","wikidata":"https://www.wikidata.org/wiki/Q168388","display_name":"Adaptive filter","level":2,"score":0.2533999979496002}],"mesh":[{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000098415","descriptor_name":"Convolutional Neural Networks","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000098415","descriptor_name":"Convolutional Neural Networks","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000098415","descriptor_name":"Convolutional Neural Networks","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000098429","descriptor_name":"Classification Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000098429","descriptor_name":"Classification Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000098429","descriptor_name":"Classification Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000098437","descriptor_name":"Prediction Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000098437","descriptor_name":"Prediction Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000098437","descriptor_name":"Prediction Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D003324","descriptor_name":"Coronary Artery Disease","qualifier_ui":"Q000145","qualifier_name":"classification","is_major_topic":true},{"descriptor_ui":"D003324","descriptor_name":"Coronary Artery Disease","qualifier_ui":"Q000145","qualifier_name":"classification","is_major_topic":true},{"descriptor_ui":"D003324","descriptor_name":"Coronary Artery Disease","qualifier_ui":"Q000145","qualifier_name":"classification","is_major_topic":true},{"descriptor_ui":"D003324","descriptor_name":"Coronary Artery Disease","qualifier_ui":"Q000175","qualifier_name":"diagnosis","is_major_topic":true},{"descriptor_ui":"D003324","descriptor_name":"Coronary Artery Disease","qualifier_ui":"Q000175","qualifier_name":"diagnosis","is_major_topic":true},{"descriptor_ui":"D003324","descriptor_name":"Coronary Artery Disease","qualifier_ui":"Q000175","qualifier_name":"diagnosis","is_major_topic":true},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":3,"locations":[{"id":"doi:10.1371/journal.pone.0353605","doi":"https://doi.org/10.1371/journal.pone.0353605","is_oa":true,"landing_page_url":"https://doi.org/10.1371/journal.pone.0353605","pdf_url":"https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0353605&type=printable","source":{"id":"https://openalex.org/S202381698","display_name":"PLoS ONE","issn_l":"1932-6203","issn":["1932-6203"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","dongbi-c","doyens","erih-plus","jufo-1","ki-jl-1","medline","norway-1","poland-100","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310315706","host_organization_name":"Public Library of Science","host_organization_lineage":["https://openalex.org/P4310315706"],"host_organization_lineage_names":["Public Library of Science"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"PLOS One","raw_type":"journal-article"},{"id":"pmid:42467701","doi":null,"is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/42467701","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"PloS one","raw_type":"Journal Article"},{"id":"pmh:oai:pubmedcentral.nih.gov:13379087","doi":null,"is_oa":true,"landing_page_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13379087/","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"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":"PLoS One","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.1371/journal.pone.0353605","doi":"https://doi.org/10.1371/journal.pone.0353605","is_oa":true,"landing_page_url":"https://doi.org/10.1371/journal.pone.0353605","pdf_url":"https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0353605&type=printable","source":{"id":"https://openalex.org/S202381698","display_name":"PLoS ONE","issn_l":"1932-6203","issn":["1932-6203"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","dongbi-c","doyens","erih-plus","jufo-1","ki-jl-1","medline","norway-1","poland-100","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310315706","host_organization_name":"Public Library of Science","host_organization_lineage":["https://openalex.org/P4310315706"],"host_organization_lineage_names":["Public Library of Science"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"PLOS One","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3","score":0.5683000087738037}],"sustainable_development_goals_aurora":[{"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3","score":0.8486839532852173}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7169588331.pdf","grobid_xml":"https://content.openalex.org/works/W7169588331.grobid-xml"},"referenced_works_count":32,"referenced_works":["https://openalex.org/W2965364867","https://openalex.org/W3009351641","https://openalex.org/W3014647989","https://openalex.org/W3039665270","https://openalex.org/W3083282610","https://openalex.org/W3085858072","https://openalex.org/W3087568108","https://openalex.org/W3092849554","https://openalex.org/W3120777667","https://openalex.org/W3159513182","https://openalex.org/W3199085549","https://openalex.org/W3217155453","https://openalex.org/W4206102576","https://openalex.org/W4210417332","https://openalex.org/W4220715927","https://openalex.org/W4281485926","https://openalex.org/W4281718985","https://openalex.org/W4290861058","https://openalex.org/W4291302421","https://openalex.org/W4310858798","https://openalex.org/W4318256698","https://openalex.org/W4323644141","https://openalex.org/W4390572267","https://openalex.org/W4391430340","https://openalex.org/W4396517557","https://openalex.org/W4399665930","https://openalex.org/W4402075485","https://openalex.org/W4402604371","https://openalex.org/W4406520152","https://openalex.org/W4409310094","https://openalex.org/W4412066410","https://openalex.org/W4416973023"],"related_works":[],"abstract_inverted_index":{"Coronary":[0],"Artery":[1],"Disease":[2],"(CAD)":[3],"is":[4,12,33],"a":[5,59,111,121,193],"leading":[6],"cause":[7],"of":[8,27,39,49,74,161,173,202],"mortality":[9],"worldwide":[10],"and":[11,46,91,108,195],"primarily":[13],"associated":[14],"with":[15,124,166,169,185],"atherosclerotic":[16],"plaque":[17],"formation,":[18],"resulting":[19],"in":[20],"coronary":[21,112,203],"artery":[22,113,204],"stenosis.":[23],"The":[24,80,95,116,136],"accurate":[25],"prediction":[26,78],"CAD":[28,77],"using":[29,110,127,176],"deep":[30,75],"learning":[31],"models":[32,125],"often":[34],"constrained":[35],"by":[36,164],"the":[37,50,72,128,141,149,158,170,189,200],"limitations":[38],"conventional":[40,129],"optimization":[41,183],"techniques,":[42],"including":[43,104],"premature":[44],"convergence":[45],"limited":[47],"adaptability":[48],"models.":[51,79,155],"To":[52],"address":[53],"these":[54],"challenges,":[55],"this":[56],"study":[57],"proposes":[58],"Dynamic":[60,143],"Optuna":[61,67,144],"Hybrid":[62,130],"Harris":[63,131],"Hawks":[64,132],"Optimization":[65,133],"(Dynamic":[66],"H-HHO)":[68],"framework":[69,96,146,191],"to":[70,99],"enhance":[71],"performance":[73,117],"learning-based":[76],"proposed":[81,142,190],"approach":[82],"integrates":[83],"dynamic":[84],"parameter":[85],"adjustment,":[86],"adaptive":[87,182],"escape":[88],"energy":[89],"mechanisms,":[90],"Optuna-based":[92],"hyperparameter":[93,187],"tuning.":[94],"was":[97,118],"applied":[98],"optimize":[100],"several":[101],"deep-learning":[102],"classifiers,":[103],"ResNet-50,":[105],"VGG-16,":[106],"InceptionV3,":[107],"MobileNet,":[109],"stenosis":[114],"dataset.":[115],"evaluated":[119,154],"through":[120],"comparative":[122],"analysis":[123],"optimized":[126],"(H-HHO)":[134],"algorithm.":[135],"experimental":[137],"results":[138],"indicate":[139],"that":[140],"H-HHO":[145],"consistently":[147],"improves":[148],"predictive":[150],"accuracy":[151,160,172,201],"across":[152],"all":[153],"InceptionV3":[156],"achieved":[157],"highest":[159],"97.9%,":[162],"followed":[163],"MobileNet":[165],"97.6%,":[167],"compared":[168],"maximum":[171],"82.46%":[174],"obtained":[175],"traditional":[177],"HHO-based":[178],"optimization.":[179],"By":[180],"combining":[181],"strategies":[184],"automated":[186],"tuning,":[188],"provides":[192],"robust":[194],"scalable":[196],"solution":[197],"for":[198],"improving":[199],"disease":[205],"prediction.":[206]},"counts_by_year":[],"updated_date":"2026-10-13T10:08:12.033832","created_date":"2026-07-18T00:00:00"}