{"id":"https://openalex.org/W4366376366","doi":"https://doi.org/10.1109/icicacs57338.2023.10099769","title":"Cardio-Vascular Disease Prediction using Machine Learning Techniques","display_name":"Cardio-Vascular Disease Prediction using Machine Learning Techniques","publication_year":2023,"publication_date":"2023-02-24","ids":{"openalex":"https://openalex.org/W4366376366","doi":"https://doi.org/10.1109/icicacs57338.2023.10099769"},"language":"en","primary_location":{"id":"doi:10.1109/icicacs57338.2023.10099769","doi":"https://doi.org/10.1109/icicacs57338.2023.10099769","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icicacs57338.2023.10099769","pdf_url":null,"source":{"id":"https://openalex.org/S7407088986","display_name":"International Conference on Integrated Circuits and Communication Systems (ICICACS)","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 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS)","raw_type":"proceedings-article"},"parent_works":[],"lineage":["https://openalex.org/W4366376366"],"child_works_count":0,"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/A5112707005","display_name":"Srinivas Konda","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4210104573","display_name":"CMR University","ror":"https://ror.org/015waqy33","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210104573","https://openalex.org/I4405282222"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Srinivas Konda","raw_affiliation_strings":["CMR Technical Campus,Department of Data Science,Hyderabad,India","Department of Data Science, CMR Technical Campus, Hyderabad, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CMR Technical Campus,Department of Data Science,Hyderabad,India","institution_ids":["https://openalex.org/I4210104573"]},{"raw_affiliation_string":"Department of Data Science, CMR Technical Campus, Hyderabad, India","institution_ids":["https://openalex.org/I4210104573"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063586291","display_name":"Naresh Kumar Kar","orcid":"https://orcid.org/0000-0003-0885-6084","observed_orcids":["https://orcid.org/0000-0003-0885-6084"]},"institutions":[{"id":"https://openalex.org/I885392262","display_name":"GITAM University","ror":"https://ror.org/0440p1d37","country_code":"IN","type":"education","lineage":["https://openalex.org/I885392262"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Naresh Kumar Kar","raw_affiliation_strings":["GITAM Deemed to be University,Department of CSE,Hyderabad,India","Department of CSE, GITAM Deemed to be University, Hyderabad, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"GITAM Deemed to be University,Department of CSE,Hyderabad,India","institution_ids":["https://openalex.org/I885392262"]},{"raw_affiliation_string":"Department of CSE, GITAM Deemed to be University, Hyderabad, India","institution_ids":["https://openalex.org/I885392262"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012763288","display_name":"Padmaja Pulicherla","orcid":"https://orcid.org/0000-0002-3256-7437","observed_orcids":["https://orcid.org/0000-0002-3256-7437"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Padmaja Pulicherla","raw_affiliation_strings":["Hyderabad Institute of Technology and Management,Department of Computer Science and Engineering,Telangana,India","Department of Computer Science and Engineering, Hyderabad Institute of Technology and Management, Telangana, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hyderabad Institute of Technology and Management,Department of Computer Science and Engineering,Telangana,India","institution_ids":[]},{"raw_affiliation_string":"Department of Computer Science and Engineering, Hyderabad Institute of Technology and Management, Telangana, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015250351","display_name":"Gandla Shivakanth","orcid":"https://orcid.org/0000-0001-6787-6929","observed_orcids":["https://orcid.org/0000-0001-6787-6929"]},"institutions":[{"id":"https://openalex.org/I875944469","display_name":"Koneru Lakshmaiah Education Foundation","ror":"https://ror.org/02k949197","country_code":"IN","type":"education","lineage":["https://openalex.org/I875944469"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Gandla Shivakanth","raw_affiliation_strings":["K L University, Vadeeshwaram,Department of Computer Science & Engineering,Vijayawada,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"K L University, Vadeeshwaram,Department of Computer Science & Engineering,Vijayawada,India","institution_ids":["https://openalex.org/I875944469"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000318042","display_name":"A C Ramachandra","orcid":"https://orcid.org/0000-0002-5868-5490","observed_orcids":["https://orcid.org/0000-0002-5868-5490"]},"institutions":[{"id":"https://openalex.org/I4405268608","display_name":"Nitte Meenakshi Institute of Technology","ror":"https://ror.org/01h4k6h92","country_code":"IN","type":"education","lineage":["https://openalex.org/I4405268608"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Ramachandra A C","raw_affiliation_strings":["Nitte Meenakshi Institute of Technology,Department of Electronics and Communication Engineering,Bengaluru,India","Department of Electronics and Communication Engineering, Nitte Meenakshi Institute of Technology, Bengaluru, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nitte Meenakshi Institute of Technology,Department of Electronics and Communication Engineering,Bengaluru,India","institution_ids":["https://openalex.org/I4405268608"]},{"raw_affiliation_string":"Department of Electronics and Communication Engineering, Nitte Meenakshi Institute of Technology, Bengaluru, India","institution_ids":["https://openalex.org/I4405268608"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.2603,"has_fulltext":false,"cited_by_count":19,"citation_normalized_percentile":{"value":0.88203753,"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":"8"},"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":9.999999747378752e-05,"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/T13038","display_name":"Internet of Things and AI","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"}}],"keywords":[{"id":"https://openalex.org/keywords/coronary-artery-disease-risk-prediction","display_name":"coronary heart disease risk prediction","score":0.8379999995231628},{"id":"https://openalex.org/keywords/cardiovascular-disease-prediction","display_name":"cardiovascular disease prediction","score":0.878000020980835},{"id":"https://openalex.org/keywords/c4-5-decision-tree","display_name":"C4.5 decision tree","score":0.9190000295639038},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"support vector machine","score":0.847000002861023},{"id":"https://openalex.org/keywords/naive-bayes","display_name":"naive Bayes","score":0.8370000123977661},{"id":"https://openalex.org/keywords/blood-pressure","display_name":"blood pressure","score":0.5429999828338623},{"id":"https://openalex.org/keywords/body-mass-index","display_name":"body mass index","score":0.9020000100135803},{"id":"https://openalex.org/keywords/blood-glucose","display_name":"blood glucose","score":0.5759999752044678},{"id":"https://openalex.org/keywords/triglycerides","display_name":"triglycerides","score":0.9629999995231628},{"id":"https://openalex.org/keywords/professional-drivers","display_name":"professional drivers","score":0.7179999947547913},{"id":"https://openalex.org/keywords/health-checkup-data","display_name":"health checkup data","score":0.8130000233650208},{"id":"https://openalex.org/keywords/risk-classification","display_name":"risk classification","score":0.3959999978542328}],"concepts":[{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.7095329761505127},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.67057204246521},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6055455207824707},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.6048439145088196},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5908838510513306},{"id":"https://openalex.org/C58471807","wikidata":"https://www.wikidata.org/wiki/Q327120","display_name":"Receiver operating characteristic","level":2,"score":0.5907557010650635},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5816004276275635},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5813806056976318},{"id":"https://openalex.org/C84393581","wikidata":"https://www.wikidata.org/wiki/Q82642","display_name":"Blood pressure","level":2,"score":0.5120849609375},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.4962959885597229},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.4466255307197571},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.41991323232650757},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3838255703449249},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.23207926750183105},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.20891249179840088},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10613968968391418},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icicacs57338.2023.10099769","doi":"https://doi.org/10.1109/icicacs57338.2023.10099769","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icicacs57338.2023.10099769","pdf_url":null,"source":{"id":"https://openalex.org/S7407088986","display_name":"International Conference on Integrated Circuits and Communication Systems (ICICACS)","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 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","score":0.98580002784729,"display_name":"Good health and well-being"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W2081785971","https://openalex.org/W2534085923","https://openalex.org/W2808906432","https://openalex.org/W2903099708","https://openalex.org/W2947717622","https://openalex.org/W2949767632","https://openalex.org/W2992625249","https://openalex.org/W2995512232","https://openalex.org/W3020201053","https://openalex.org/W3035142875","https://openalex.org/W3036712625","https://openalex.org/W3047072392","https://openalex.org/W3208539696","https://openalex.org/W4200137509","https://openalex.org/W4214605467","https://openalex.org/W4223891295","https://openalex.org/W4312578036","https://openalex.org/W4394796198"],"related_works":["https://openalex.org/W4412656779","https://openalex.org/W4312629882","https://openalex.org/W4391753980","https://openalex.org/W4378364761","https://openalex.org/W3018390422","https://openalex.org/W2227730709","https://openalex.org/W3203577970","https://openalex.org/W4213237558","https://openalex.org/W4392114454","https://openalex.org/W4315781749"],"abstract_inverted_index":{"The":[0,33,80,102,191,215,233],"main":[1],"goal":[2],"of":[3,28,64,105,149],"this":[4,51,70,100],"study":[5,107],"is":[6,58,75,158],"to":[7,16,49,182,248],"use":[8],"Data":[9],"Mining":[10],"Method":[11],"and":[12,23,38,72,94,114,123,139,170,186,230,255],"Artificial":[13],"Neural":[14],"Network":[15],"develop":[17],"a":[18,153],"system":[19],"that":[20,157,237,245],"can":[21],"automatically":[22],"rapidly":[24],"predict":[25],"the":[26,61,65,78,95,106,111,171,178,188,201,207,213,221,238,252],"risk":[27,56,189,199],"coronary":[29],"heart":[30],"disease":[31],"(ANN).":[32],"IRT":[34],"Perundurai":[35],"Medical":[36],"College":[37],"Hospital's":[39],"master":[40],"health":[41],"checkup":[42],"data":[43],"on":[44,160],"occupational":[45],"drivers":[46],"were":[47,204,246],"used":[48,98,181],"test":[50],"idea":[52],"(PMCH).":[53],"Analysis":[54],"for":[55,99],"identification":[57],"performed":[59,76],"in":[60,69,77,174,196],"first":[62],"stage":[63],"hybrid":[66],"approach":[67],"suggested":[68],"study,":[71],"level":[73],"prediction":[74],"second.":[79],"sensitivity,":[81],"specificity,":[82],"precision,":[83],"receiver":[84],"operating":[85],"curve,":[86,89],"area":[87],"under":[88],"10-fold":[90],"cross":[91],"validation":[92],"technique,":[93],"F-measure":[96],"are":[97,128,142,177],"investigation.":[101,214],"initial":[103],"step":[104],"involves":[108],"thinking":[109],"about":[110],"most":[112],"common":[113],"changeable":[115],"dangers.":[116],"Systolic":[117],"blood":[118,121,134,137,144],"pressure,":[119,122],"diastolic":[120],"body":[124],"mass":[125],"index":[126],"(BMI)":[127],"three":[129,143,179,202],"biophysical":[130],"variables,":[131],"whereas":[132],"fasting":[133],"sugar,":[135,138],"postprandial":[136],"triglyceride":[140],"levels":[141],"chemical":[145],"factors":[146],"(TG).":[147],"All":[148],"these":[150,184],"characteristics":[151],"have":[152],"predetermined":[154],"margin":[155],"value":[156],"based":[159],"WHO":[161],"guidelines.":[162],"Support":[163],"Vector":[164],"Machine":[165],"(SVM),":[166],"Naive":[167,253],"Bayes":[168,254],"(NB),":[169],"C4.5":[172,192,218],"algorithm":[173,193],"Decision":[175,239],"Tree":[176],"approaches":[180,203],"categorize":[183],"variables":[185],"forecast":[187],"(DT).":[190],"fared":[194],"best":[195],"forecasting":[197],"CHD":[198],"when":[200],"compared":[205],"using":[206],"performance":[208],"metrics,":[209],"as":[210],"discovered":[211],"by":[212,251],"decision":[216],"tree":[217,240],"method":[219,241],"outperformed":[220],"other":[222],"two":[223],"classifiers":[224],"with":[225],"an":[226],"improved":[227],"99.5%":[228],"accuracy":[229],"99.67%":[231],"sensitivity.":[232],"increased":[234],"percentage":[235],"demonstrates":[236],"delivered":[242],"consistent":[243],"results":[244],"better":[247],"those":[249],"produced":[250],"SVM":[256],"models.":[257]},"counts_by_year":[{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":2}],"updated_date":"2026-11-14T11:04:58.165337","created_date":"2023-04-21T00:00:00"}