{"id":"https://openalex.org/W4411189289","doi":"https://doi.org/10.32604/cmc.2025.065287","title":"QHF-CS: Quantum-Enhanced Heart Failure Prediction Using Quantum CNN with Optimized Feature Qubit Selection with Cuckoo Search in Skewed Clinical Data","display_name":"QHF-CS: Quantum-Enhanced Heart Failure Prediction Using Quantum CNN with Optimized Feature Qubit Selection with Cuckoo Search in Skewed Clinical Data","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4411189289","doi":"https://doi.org/10.32604/cmc.2025.065287"},"language":"en","primary_location":{"id":"doi:10.32604/cmc.2025.065287","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.065287","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"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":"Computers, Materials &amp; Continua","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.32604/cmc.2025.065287","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5083227676","display_name":"Prasanna Kottapalle","orcid":"https://orcid.org/0000-0001-8182-0163"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Prasanna Kottapalle","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Tan Kuan Tak","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tan Kuan Tak","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076524025","display_name":"Pravin R. Kshirsagar","orcid":"https://orcid.org/0000-0002-7381-6284"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pravin Ramdas Kshirsagar","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Gopichand Ginnela","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gopichand Ginnela","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5116494434","display_name":"Vijaya Krishna Akula","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vijaya Krishna Akula","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5083227676"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":7.9762,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.97144986,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":"84","issue":"2","first_page":"3857","last_page":"3892"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.8784999847412109,"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":0.8784999847412109,"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/T12702","display_name":"Brain Tumor Detection and Classification","score":0.8091999888420105,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/cuckoo-search","display_name":"Cuckoo search","score":0.7510143518447876},{"id":"https://openalex.org/keywords/qubit","display_name":"Qubit","score":0.6397701501846313},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.6140202283859253},{"id":"https://openalex.org/keywords/quantum","display_name":"Quantum","score":0.5625900626182556},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5335854291915894},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.49174025654792786},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.47858619689941406},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.33869117498397827},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3133319616317749},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3030658960342407},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.17131829261779785},{"id":"https://openalex.org/keywords/quantum-mechanics","display_name":"Quantum mechanics","score":0.12530651688575745}],"concepts":[{"id":"https://openalex.org/C117241572","wikidata":"https://www.wikidata.org/wiki/Q5192379","display_name":"Cuckoo search","level":3,"score":0.7510143518447876},{"id":"https://openalex.org/C203087015","wikidata":"https://www.wikidata.org/wiki/Q378201","display_name":"Qubit","level":3,"score":0.6397701501846313},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.6140202283859253},{"id":"https://openalex.org/C84114770","wikidata":"https://www.wikidata.org/wiki/Q46344","display_name":"Quantum","level":2,"score":0.5625900626182556},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5335854291915894},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.49174025654792786},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.47858619689941406},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33869117498397827},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3133319616317749},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3030658960342407},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.17131829261779785},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.12530651688575745},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C85617194","wikidata":"https://www.wikidata.org/wiki/Q2072794","display_name":"Particle swarm optimization","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.32604/cmc.2025.065287","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.065287","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"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":"Computers, Materials &amp; Continua","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.32604/cmc.2025.065287","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.065287","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"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":"Computers, Materials &amp; Continua","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","score":0.5199999809265137,"display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W1964176984","https://openalex.org/W1991224261","https://openalex.org/W2103956991","https://openalex.org/W2111084110","https://openalex.org/W2513075559","https://openalex.org/W2559394418","https://openalex.org/W2796293949","https://openalex.org/W2798434869","https://openalex.org/W2887925010","https://openalex.org/W2922968028","https://openalex.org/W2971576504","https://openalex.org/W2972032089","https://openalex.org/W2980446414","https://openalex.org/W3106887093","https://openalex.org/W3110449665","https://openalex.org/W3135279508","https://openalex.org/W3160768652","https://openalex.org/W3191359713","https://openalex.org/W3192227636","https://openalex.org/W4200398261","https://openalex.org/W4206799920","https://openalex.org/W4226369590","https://openalex.org/W4254557128","https://openalex.org/W4281738949","https://openalex.org/W4309711093","https://openalex.org/W4378222347","https://openalex.org/W4385607217","https://openalex.org/W4388494595","https://openalex.org/W4391750466","https://openalex.org/W4393315553","https://openalex.org/W4399857653","https://openalex.org/W4400721535","https://openalex.org/W4401982728","https://openalex.org/W4407963606","https://openalex.org/W4413743014"],"related_works":["https://openalex.org/W1978568284","https://openalex.org/W2742451303","https://openalex.org/W2102833174","https://openalex.org/W2753354457","https://openalex.org/W2329556607","https://openalex.org/W3013665388","https://openalex.org/W2905415873","https://openalex.org/W2901222946","https://openalex.org/W4386564352","https://openalex.org/W2952668426"],"abstract_inverted_index":{"Heart":[0,102],"failure":[1,231],"prediction":[2,64,78],"is":[3],"crucial":[4],"as":[5],"cardiovascular":[6],"diseases":[7],"become":[8],"the":[9,17,33,60,166,196],"leading":[10],"cause":[11],"of":[12,35],"death":[13],"worldwide,":[14],"exacerbated":[15],"by":[16,139,163,233],"COVID-19":[18],"pandemic.":[19],"Age,":[20],"cholesterol,":[21],"and":[22,41,50,52,56,73,84,93,149,169,194,218,224,237],"blood":[23],"pressure":[24],"datasets":[25,43],"are":[26],"becoming":[27],"inadequate":[28],"because":[29],"they":[30],"cannot":[31],"capture":[32],"complexity":[34,72],"emerging":[36],"health":[37,63],"indicators.":[38],"These":[39,95],"high-dimensional":[40,202],"heterogeneous":[42],"make":[44],"traditional":[45,77],"machine":[46],"learning":[47],"methods":[48],"difficult,":[49],"Skewness":[51],"other":[53],"new":[54],"biomarkers":[55],"psychosocial":[57],"factors":[58],"bias":[59],"model\u2019s":[61],"heart":[62,230],"across":[65],"diverse":[66],"patient":[67],"profiles.":[68],"Modern":[69],"medical":[70],"datasets\u2019":[71],"high":[74],"dimensionality":[75],"challenge":[76],"models":[79,209],"like":[80,191],"Support":[81],"Vector":[82],"Machines":[83],"Decision":[85],"Trees.":[86],"Quantum":[87,106,131,226],"approaches":[88],"include":[89],"QSVM,":[90],"QkNN,":[91],"QDT,":[92],"others.":[94],"Constraints":[96],"drove":[97],"research.":[98,125],"The":[99,211],"\u201cQHF-CS:":[100],"Quantum-Enhanced":[101],"Failure":[103],"Prediction":[104],"using":[105],"CNN":[107],"with":[108,113],"Optimized":[109],"Feature":[110],"Qubit":[111],"Selection":[112],"Cuckoo":[114],"Search":[115,142],"in":[116,123],"Skewed":[117],"Clinical":[118],"Data\u201d":[119],"system":[120,128],"was":[121],"developed":[122],"this":[124],"This":[126],"novel":[127],"leverages":[129],"a":[130],"Convolutional":[132],"Neural":[133],"Network":[134],"(QCNN)-based":[135],"quantum":[136,179,187],"circuit,":[137],"enhanced":[138],"meta-heuristic":[140],"algorithms\u2014Cuckoo":[141],"Optimization":[143,152],"(CSO),":[144],"Artificial":[145],"Bee":[146],"Colony":[147],"(ABC),":[148],"Particle":[150],"Swarm":[151],"(PSO)\u2014for":[153],"feature":[154,172,189],"qubit":[155],"selection.":[156],"Among":[157],"these,":[158],"CSO":[159],"demonstrated":[160],"superior":[161],"performance":[162],"consistently":[164],"identifying":[165],"most":[167],"optimal":[168],"least":[170],"skewed":[171],"subsets,":[173],"which":[174],"were":[175],"then":[176],"encoded":[177],"into":[178],"states":[180],"for":[181],"circuit":[182,188],"construction.":[183],"By":[184],"integrating":[185],"advanced":[186],"maps":[190],"ZZFeatureMap,":[192],"RealAmplitudes,":[193],"EfficientSU2,":[195],"QHF-CS":[197,212],"model":[198,213,235],"efficiently":[199],"processes":[200],"complex,":[201],"data,":[203],"capturing":[204],"intricate":[205],"patterns":[206],"that":[207],"classical":[208],"overlook.":[210],"improves":[214],"precision,":[215],"recall,":[216],"F1-score,":[217],"accuracy":[219,236],"to":[220],"0.94,":[221,223],"0.95,":[222],"0.94.":[225],"computing":[227],"could":[228],"revolutionize":[229],"diagnostics":[232],"improving":[234],"computational":[238],"efficiency,":[239],"enabling":[240],"complex":[241],"healthcare":[242],"diagnostic":[243],"breakthroughs.":[244]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":1}],"updated_date":"2026-04-18T07:56:08.524223","created_date":"2025-10-10T00:00:00"}
