{"id":"https://openalex.org/W2901980396","doi":"https://doi.org/10.1002/cpe.5079","title":"Clustering\u2010based approach for medical data classification","display_name":"Clustering\u2010based approach for medical data classification","publication_year":2018,"publication_date":"2018-11-22","ids":{"openalex":"https://openalex.org/W2901980396","doi":"https://doi.org/10.1002/cpe.5079","mag":"2901980396"},"language":"en","primary_location":{"id":"doi:10.1002/cpe.5079","doi":"https://doi.org/10.1002/cpe.5079","is_oa":false,"landing_page_url":"https://doi.org/10.1002/cpe.5079","pdf_url":null,"source":{"id":"https://openalex.org/S11065456","display_name":"Concurrency and Computation Practice and Experience","issn_l":"1532-0626","issn":["1532-0626","1532-0634"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","ccf-c","cwts-core","dongbi-d","jufo-1","norway-1","poland-100","russia-white-list-2"],"host_organization":"https://openalex.org/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Concurrency and Computation: Practice and Experience","raw_type":"journal-article"},"volume":null,"type":"article","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/A5085175147","display_name":"Mallikarjun M Kodabagi","orcid":"https://orcid.org/0000-0002-9947-6708","observed_orcids":["https://orcid.org/0000-0002-9947-6708"]},"institutions":[{"id":"https://openalex.org/I83737708","display_name":"REVA University","ror":"https://ror.org/03gtcxd54","country_code":"IN","type":"education","lineage":["https://openalex.org/I83737708"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Mallikarjun M. Kodabagi","raw_affiliation_strings":["School of Computing and IT, Reva University, Bangalore, India","Mallikarjun M. Kodabagi, School of Computing and IT, Reva University, Bangalore-560 064, India"],"raw_orcid":"https://orcid.org/0000-0002-9947-6708","affiliations":[{"raw_affiliation_string":"School of Computing and IT, Reva University, Bangalore, India","institution_ids":["https://openalex.org/I83737708"]},{"raw_affiliation_string":"Mallikarjun M. Kodabagi, School of Computing and IT, Reva University, Bangalore-560 064, India","institution_ids":["https://openalex.org/I83737708"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086996582","display_name":"Ahelam Tikotikar","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I83737708","display_name":"REVA University","ror":"https://ror.org/03gtcxd54","country_code":"IN","type":"education","lineage":["https://openalex.org/I83737708"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Ahelam Tikotikar","raw_affiliation_strings":["School of Computing and IT, Reva University, Bangalore, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computing and IT, Reva University, Bangalore, India","institution_ids":["https://openalex.org/I83737708"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5085175147"],"corresponding_institution_ids":["https://openalex.org/I83737708"],"apc_list":{"value":5140,"currency":"USD","value_usd":5140},"apc_paid":null,"fwci":0.7496,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.74510782,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":95},"biblio":{"volume":"31","issue":"14","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.9945999979972839,"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":0.9945999979972839,"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":0.002199999988079071,"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/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.0017000000225380063,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/medical-data-classification","display_name":"medical data classification","score":0.8849999904632568},{"id":"https://openalex.org/keywords/clinical-decision-support-systems","display_name":"clinical decision support systems","score":0.878000020980835},{"id":"https://openalex.org/keywords/disease-identification","display_name":"disease identification","score":0.6380000114440918}],"concepts":[{"id":"https://openalex.org/C146599234","wikidata":"https://www.wikidata.org/wiki/Q511093","display_name":"Centroid","level":2,"score":0.6431476473808289},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6343863010406494},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6305544972419739},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5946349501609802},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5101006627082825},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4746197760105133},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4624858498573303},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.46057793498039246}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1002/cpe.5079","doi":"https://doi.org/10.1002/cpe.5079","is_oa":false,"landing_page_url":"https://doi.org/10.1002/cpe.5079","pdf_url":null,"source":{"id":"https://openalex.org/S11065456","display_name":"Concurrency and Computation Practice and Experience","issn_l":"1532-0626","issn":["1532-0626","1532-0634"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","ccf-c","cwts-core","dongbi-d","jufo-1","norway-1","poland-100","russia-white-list-2"],"host_organization":"https://openalex.org/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Concurrency and Computation: Practice and Experience","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5683000087738037,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1977230515","https://openalex.org/W2014784356","https://openalex.org/W2081434183","https://openalex.org/W2205358384","https://openalex.org/W2232748179","https://openalex.org/W2234984784","https://openalex.org/W2268017380","https://openalex.org/W2273920155","https://openalex.org/W2276849971","https://openalex.org/W2310231101","https://openalex.org/W2505975774","https://openalex.org/W2592717708","https://openalex.org/W2599061756","https://openalex.org/W2674025757","https://openalex.org/W2918675550"],"related_works":["https://openalex.org/W2417567428","https://openalex.org/W1974183452","https://openalex.org/W2101710235","https://openalex.org/W4237294272","https://openalex.org/W1587935247","https://openalex.org/W2474832486","https://openalex.org/W4316468748","https://openalex.org/W2088249484","https://openalex.org/W2104161577","https://openalex.org/W3178156511"],"abstract_inverted_index":{"Summary":[0],"Medical":[1,62],"data":[2,35,58,134],"records":[3,36],"are":[4,126],"growing":[5],"enormously":[6],"in":[7,148],"present":[8],"days":[9],"due":[10],"to":[11,32,37,51],"the":[12,21,43,53,94,97,104,110,124,129],"growth":[13],"of":[14,20,45,55,83,89,91,103,122,150],"various":[15],"medical":[16,30,57,85,133],"technologies":[17],"and":[18,39,153],"population":[19],"globe.":[22],"It":[23],"is":[24,49,79,113],"a":[25,29],"difficult":[26],"task":[27],"for":[28,81,87,101,132],"expert":[31],"process":[33],"these":[34],"identify":[38],"provide":[40],"treatment":[41],"regarding":[42],"disease":[44,90],"patient.":[46],"Hence,":[47],"it":[48],"necessary":[50],"automate":[52],"processing":[54],"such":[56],"using":[59,106,115],"machine":[60],"learning\u2010based":[61],"Decision":[63],"Support":[64],"System.":[65],"In":[66],"this":[67],"proposed":[68],"work,":[69],"an":[70],"Effective":[71],"Fuzzy":[72],"Rule":[73],"Classifier":[74],"(EFRC)\u2013based":[75],"decision":[76],"support":[77],"system":[78],"used":[80,127],"analysis":[82],"UCI":[84],"dataset":[86],"identification":[88],"patients.":[92],"Initially,":[93],"method":[95],"determines":[96],"best":[98],"centroid":[99,111,120],"value":[100,112],"each":[102],"attribute":[105],"training":[107],"dataset.":[108],"Furthermore,":[109],"refined":[114,119],"test":[116],"samples.":[117],"The":[118],"values":[121],"all":[123],"attributes":[125],"by":[128],"fuzzy":[130],"classifier":[131],"classification.":[135],"Experimental":[136],"results":[137],"have":[138],"proven":[139],"that":[140],"EFRC":[141],"performs":[142],"better":[143],"classification":[144],"than":[145],"existing":[146],"systems":[147],"terms":[149],"accuracy,":[151],"sensitivity,":[152],"specificity.":[154]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1}],"updated_date":"2026-10-30T12:39:25.321706","created_date":"2018-11-29T00:00:00"}
