{"id":"https://openalex.org/W3109378215","doi":"https://doi.org/10.1109/icstcc50638.2020.9259719","title":"Angiographic prognosis and diagnosis of heart disease by using unsupervised and supervised Machine Learning techniques","display_name":"Angiographic prognosis and diagnosis of heart disease by using unsupervised and supervised Machine Learning techniques","publication_year":2020,"publication_date":"2020-10-08","ids":{"openalex":"https://openalex.org/W3109378215","doi":"https://doi.org/10.1109/icstcc50638.2020.9259719","mag":"3109378215"},"language":"en","primary_location":{"id":"doi:10.1109/icstcc50638.2020.9259719","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icstcc50638.2020.9259719","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 24th International Conference on System Theory, Control and Computing (ICSTCC)","raw_type":"proceedings-article"},"type":"article","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/A5036496763","display_name":"Sebastian Sb\u00eern\u0103","orcid":null},"institutions":[{"id":"https://openalex.org/I96673099","display_name":"Technical University of Denmark","ror":"https://ror.org/04qtj9h94","country_code":"DK","type":"education","lineage":["https://openalex.org/I96673099"]}],"countries":["DK"],"is_corresponding":true,"raw_author_name":"Sebastian Sbirna","raw_affiliation_strings":["DTU Compute Department, Institute of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark"],"affiliations":[{"raw_affiliation_string":"DTU Compute Department, Institute of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark","institution_ids":["https://openalex.org/I96673099"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003219948","display_name":"Liana-Simona Sb\u00eern\u0103","orcid":null},"institutions":[{"id":"https://openalex.org/I97553796","display_name":"University of Craiova","ror":"https://ror.org/03s251g81","country_code":"RO","type":"education","lineage":["https://openalex.org/I97553796"]}],"countries":["RO"],"is_corresponding":false,"raw_author_name":"Liana-Simona Sbirna","raw_affiliation_strings":["Department of Chemistry, Faculty of Sciences, University of Craiova, Craiova, Romania"],"affiliations":[{"raw_affiliation_string":"Department of Chemistry, Faculty of Sciences, University of Craiova, Craiova, Romania","institution_ids":["https://openalex.org/I97553796"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5036496763"],"corresponding_institution_ids":["https://openalex.org/I96673099"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.20790127,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":"3","issue":null,"first_page":"84","last_page":"91"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.9944000244140625,"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":0.9944000244140625,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.975600004196167,"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/T12368","display_name":"Grey System Theory Applications","score":0.9545000195503235,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7801491022109985},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.7328247427940369},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7090930938720703},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6233267188072205},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.6214785575866699},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5760140419006348},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.5555638074874878},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.513127863407135},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.48296040296554565},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.4598771631717682},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.4579530954360962},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.43331044912338257},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08078587055206299}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7801491022109985},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7328247427940369},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7090930938720703},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6233267188072205},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.6214785575866699},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5760140419006348},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.5555638074874878},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.513127863407135},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.48296040296554565},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.4598771631717682},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.4579530954360962},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43331044912338257},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08078587055206299},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icstcc50638.2020.9259719","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icstcc50638.2020.9259719","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 24th International Conference on System Theory, Control and Computing (ICSTCC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5299999713897705,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W1989164753","https://openalex.org/W2050206309","https://openalex.org/W2151572298","https://openalex.org/W2186106806","https://openalex.org/W2988864014","https://openalex.org/W6682118690","https://openalex.org/W6762912957"],"related_works":["https://openalex.org/W2499612753","https://openalex.org/W3111802945","https://openalex.org/W2946096271","https://openalex.org/W2295423552","https://openalex.org/W1598471830","https://openalex.org/W3107369729","https://openalex.org/W4285233543","https://openalex.org/W4230838436","https://openalex.org/W3148060700","https://openalex.org/W3080681248"],"abstract_inverted_index":{"The":[0,110],"present":[1],"work":[2],"provides":[3],"a":[4,44,82,106,122,141],"detailed":[5],"analysis":[6,114],"of":[7,80,97,112,139],"machine":[8,126],"learning":[9,30,71,127],"methods":[10],"which":[11,41],"can":[12],"successfully":[13],"be":[14],"used":[15],"in":[16,43,75],"discovering":[17],"and":[18,47,63,85,92,100,150],"predicting":[19,117],"coronary":[20,118],"heart":[21,119],"disease":[22,120],"from":[23,136],"datasets":[24,138],"containing":[25],"human":[26],"body":[27],"parameters.":[28],"Unsupervised":[29],"techniques":[31],"are":[32,148],"responsible":[33],"for":[34,125],"detecting":[35],"patterns":[36],"linking":[37],"together":[38],"medical":[39,83],"parameters":[40],"result":[42],"common":[45],"outcome,":[46],"our":[48,113],"paper":[49],"details":[50],"three":[51],"such":[52,81],"approaches:":[53],"clustering":[54],"(GMM,":[55],"hierarchical),":[56],"outlier":[57],"detection":[58],"(KDE,":[59],"KNN":[60],"density,":[61],"KNN-ARD)":[62],"association":[64],"mining.":[65],"At":[66],"the":[67,77,95,146,151],"same":[68],"time,":[69],"supervised":[70],"classifiers":[72],"will":[73],"help":[74],"understanding":[76],"prediction":[78],"possibilities":[79],"condition,":[84],"this":[86],"has":[87],"been":[88],"done":[89],"by":[90],"applying":[91],"statistically":[93],"evaluating":[94],"performance":[96],"logistic":[98],"regression":[99],"artificial":[101],"neural":[102],"networks":[103],"(ANN)":[104],"against":[105],"predictive":[107],"baseline":[108],"model.":[109],"results":[111],"show":[115],"that":[116,145],"is":[121,154],"well-suited":[123],"task":[124],"detection,":[128],"with":[129],"small":[130],"error":[131],"ranges":[132],"being":[133],"achieved":[134],"even":[135],"low-dimension":[137],"only":[140],"dozen":[142],"features,":[143],"provided":[144],"features":[147],"relevant":[149],"patient":[152],"data":[153],"well-recorded.":[155]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
