{"id":"https://openalex.org/W4362544985","doi":"https://doi.org/10.1109/piecon56912.2023.10085781","title":"Hybrid Machine Learning Algorithms for Optimal Diagnosis of Heart Disease with Feature Analysis","display_name":"Hybrid Machine Learning Algorithms for Optimal Diagnosis of Heart Disease with Feature Analysis","publication_year":2023,"publication_date":"2023-02-10","ids":{"openalex":"https://openalex.org/W4362544985","doi":"https://doi.org/10.1109/piecon56912.2023.10085781"},"language":"en","primary_location":{"id":"doi:10.1109/piecon56912.2023.10085781","doi":"https://doi.org/10.1109/piecon56912.2023.10085781","is_oa":false,"landing_page_url":"https://doi.org/10.1109/piecon56912.2023.10085781","pdf_url":null,"source":{"id":"https://openalex.org/S7407097149","display_name":"International Conference on Power, Instrumentation, Energy and Control (PIECON)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 International Conference on Power, Instrumentation, Energy and Control (PIECON)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5067806446","display_name":"Ghulab Nabi Ahmad","orcid":"https://orcid.org/0000-0002-5496-3019","observed_orcids":["https://orcid.org/0000-0002-5496-3019"]},"institutions":[{"id":"https://openalex.org/I244923355","display_name":"Mangalayatan University","ror":"https://ror.org/0563h0629","country_code":"IN","type":"education","lineage":["https://openalex.org/I244923355"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Ghulab Nabi Ahmad","raw_affiliation_strings":["Institute of Applied Sciences, Mangalayatan University, Aligarh, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Applied Sciences, Mangalayatan University, Aligarh, India","institution_ids":["https://openalex.org/I244923355"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Hira Fatima","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I244923355","display_name":"Mangalayatan University","ror":"https://ror.org/0563h0629","country_code":"IN","type":"education","lineage":["https://openalex.org/I244923355"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Hira Fatima","raw_affiliation_strings":["Institute of Applied Sciences, Mangalayatan University, Aligarh, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Applied Sciences, Mangalayatan University, Aligarh, India","institution_ids":["https://openalex.org/I244923355"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064575694","display_name":"Shafiullah","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I236004819","display_name":"Babasaheb Bhimrao Ambedkar Bihar University","ror":"https://ror.org/04jgpa018","country_code":"IN","type":"education","lineage":["https://openalex.org/I236004819"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Shafiullah","raw_affiliation_strings":["BRA, Bihar University,K.C.T.C College, Raxual,Department of Mathematics,Muzaffarpur,India","Department of Mathematics, K.C.T.C College, Raxual, BRA, Bihar University, Muzaffarpur, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"BRA, Bihar University,K.C.T.C College, Raxual,Department of Mathematics,Muzaffarpur,India","institution_ids":["https://openalex.org/I236004819"]},{"raw_affiliation_string":"Department of Mathematics, K.C.T.C College, Raxual, BRA, Bihar University, Muzaffarpur, India","institution_ids":["https://openalex.org/I236004819"]}]},{"author_position":"last","author":{"id":null,"display_name":"Mohammad Haris","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mohammad Haris","raw_affiliation_strings":["Institute of Technology and Management,Department of Computer Science and Engineering,Aligarh,India","Department of Computer Science and Engineering, Institute of Technology and Management, Aligarh, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Technology and Management,Department of Computer Science and Engineering,Aligarh,India","institution_ids":[]},{"raw_affiliation_string":"Department of Computer Science and Engineering, Institute of Technology and Management, Aligarh, India","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.3708,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.79163315,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":1.0,"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":1.0,"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/T13373","display_name":"Data Mining and Machine Learning Applications","score":0.0,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T11021","display_name":"ECG Monitoring and Analysis","score":0.0,"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/heart-disease-prediction","display_name":"heart disease prediction","score":0.9900000095367432},{"id":"https://openalex.org/keywords/hybrid-machine-learning","display_name":"hybrid machine learning","score":0.6399999856948853},{"id":"https://openalex.org/keywords/cleveland-heart-disease-dataset","display_name":"Cleveland heart disease dataset","score":0.7799999713897705},{"id":"https://openalex.org/keywords/k-nearest-neighbors","display_name":"k-nearest neighbors","score":0.7950000166893005},{"id":"https://openalex.org/keywords/random-forest","display_name":"random forest","score":0.9980000257492065},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"support vector machine","score":0.7839999794960022},{"id":"https://openalex.org/keywords/decision-tree","display_name":"decision tree","score":0.7429999709129333},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"logistic regression","score":0.777999997138977},{"id":"https://openalex.org/keywords/naive-bayes","display_name":"naive Bayes","score":0.9129999876022339},{"id":"https://openalex.org/keywords/feature-analysis","display_name":"feature analysis","score":0.7049999833106995},{"id":"https://openalex.org/keywords/k-fold-cross-validation","display_name":"k-fold cross-validation","score":0.753000020980835},{"id":"https://openalex.org/keywords/classification-accuracy","display_name":"classification accuracy","score":0.39899998903274536}],"concepts":[{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.8346725106239319},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.7579224705696106},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.729750394821167},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.717721700668335},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.7056891918182373},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6983636021614075},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6565040946006775},{"id":"https://openalex.org/C27181475","wikidata":"https://www.wikidata.org/wiki/Q541014","display_name":"Cross-validation","level":2,"score":0.5789929628372192},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.5035416483879089},{"id":"https://openalex.org/C58471807","wikidata":"https://www.wikidata.org/wiki/Q327120","display_name":"Receiver operating characteristic","level":2,"score":0.48434412479400635},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.44970864057540894},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.4418477416038513},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4313224256038666},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.4191345274448395},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3274107575416565}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/piecon56912.2023.10085781","doi":"https://doi.org/10.1109/piecon56912.2023.10085781","is_oa":false,"landing_page_url":"https://doi.org/10.1109/piecon56912.2023.10085781","pdf_url":null,"source":{"id":"https://openalex.org/S7407097149","display_name":"International Conference on Power, Instrumentation, Energy and Control (PIECON)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 International Conference on Power, Instrumentation, Energy and Control (PIECON)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being","score":0.9763000011444092}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1472067","https://openalex.org/W115552564","https://openalex.org/W1130077638","https://openalex.org/W2167672600","https://openalex.org/W2508496315","https://openalex.org/W2521029800","https://openalex.org/W2612952973","https://openalex.org/W2783900457","https://openalex.org/W2795340517","https://openalex.org/W2900794383","https://openalex.org/W2949767632","https://openalex.org/W2954788759","https://openalex.org/W3001719400","https://openalex.org/W3023591976","https://openalex.org/W3043301401","https://openalex.org/W3092745004","https://openalex.org/W3116896278","https://openalex.org/W3129307982","https://openalex.org/W3161241668","https://openalex.org/W4206658128","https://openalex.org/W4213222881","https://openalex.org/W4220736971","https://openalex.org/W4226245662","https://openalex.org/W4281482167"],"related_works":["https://openalex.org/W4381569929","https://openalex.org/W4313158877","https://openalex.org/W3048265616","https://openalex.org/W4376866891","https://openalex.org/W4297545274","https://openalex.org/W7215054863","https://openalex.org/W4399528977","https://openalex.org/W4398545733","https://openalex.org/W3112056206","https://openalex.org/W3110454747"],"abstract_inverted_index":{"Timely":[0],"prediction":[1,123],"of":[2,58,73,86,113,136,144],"heart":[3,45,64,121],"disease":[4,65,122],"and":[5,38,91,101,125,152],"its":[6],"cause":[7],"is":[8,67,77,89,93,127,139,147],"the":[9,56,71,74,80,83,99,110,114,131,134],"most":[10,102],"challenging":[11],"issue":[12],"in":[13,79],"medical":[14],"science.":[15],"This":[16],"paper":[17],"uses":[18,49],"various":[19],"machine":[20],"learning":[21],"algorithms":[22],"such":[23],"as":[24],"Logistic":[25],"Regression,":[26],"Support":[27],"Vector":[28],"Machine,":[29],"Decision":[30],"Tree,":[31],"Random":[32,96],"Forest,":[33],"Naive":[34],"Bayes,":[35],"K-Nearest":[36,87,137],"Neighbourhood,":[37],"K-fold":[39,51],"cross-validation":[40,52],"are":[41,117],"used":[42,68],"to":[43,54,69,148],"predict":[44],"diseases.":[46],"The":[47,60,95,141],"system":[48],"a":[50,150],"technique":[53],"enhance":[55],"accuracies":[57],"algorithms.":[59],"UCI":[61],"Kaggle":[62],"Cleveland":[63],"datasets":[66],"analyse":[70],"performance":[72,135],"models.":[75],"It":[76],"found":[78,128],"experiment":[81],"that":[82,129],"training":[84],"accuracy":[85],"Neighbour":[88,138],"88.52%,":[90],"Recall":[92],"93.30%.":[94],"Forest":[97],"produced":[98],"highest":[100],"comparable":[103],"Receiver":[104],"Operating":[105],"Characteristics":[106],"Curve":[107],"accuracy.":[108],"Moreover,":[109],"experimental":[111],"results":[112],"recommended":[115],"techniques":[116],"compared":[118],"with":[119],"previous":[120],"studies,":[124],"it":[126],"among":[130],"suggested":[132],"technique,":[133],"best.":[140],"fundamental":[142],"goal":[143],"this":[145],"study":[146],"design":[149],"novel":[151],"distinctive":[153],"model-creation":[154],"approach":[155],"for":[156],"resolving":[157],"practical":[158],"issues.":[159]},"counts_by_year":[{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":1}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2023-04-06T00:00:00"}