{"id":"https://openalex.org/W3115556070","doi":"https://doi.org/10.1109/uemcon51285.2020.9298051","title":"Automated prediction of Heart disease using optimized machine learning techniques","display_name":"Automated prediction of Heart disease using optimized machine learning techniques","publication_year":2020,"publication_date":"2020-10-28","ids":{"openalex":"https://openalex.org/W3115556070","doi":"https://doi.org/10.1109/uemcon51285.2020.9298051","mag":"3115556070"},"language":"en","primary_location":{"id":"doi:10.1109/uemcon51285.2020.9298051","is_oa":false,"landing_page_url":"https://doi.org/10.1109/uemcon51285.2020.9298051","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","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/A5039240754","display_name":"Lama A. Alqahtani","orcid":null},"institutions":[{"id":"https://openalex.org/I76571253","display_name":"Imam Abdulrahman Bin Faisal University","ror":"https://ror.org/038cy8j79","country_code":"SA","type":"education","lineage":["https://openalex.org/I76571253"]}],"countries":["SA"],"is_corresponding":false,"raw_author_name":"Lama A. Alqahtani","raw_affiliation_strings":["Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia","institution_ids":["https://openalex.org/I76571253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070621889","display_name":"Hanadi M. Alotaibi","orcid":null},"institutions":[{"id":"https://openalex.org/I76571253","display_name":"Imam Abdulrahman Bin Faisal University","ror":"https://ror.org/038cy8j79","country_code":"SA","type":"education","lineage":["https://openalex.org/I76571253"]}],"countries":["SA"],"is_corresponding":false,"raw_author_name":"Hanadi M. Alotaibi","raw_affiliation_strings":["Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia","institution_ids":["https://openalex.org/I76571253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033964908","display_name":"Irfan Ullah Khan","orcid":"https://orcid.org/0000-0003-1002-6178"},"institutions":[{"id":"https://openalex.org/I76571253","display_name":"Imam Abdulrahman Bin Faisal University","ror":"https://ror.org/038cy8j79","country_code":"SA","type":"education","lineage":["https://openalex.org/I76571253"]}],"countries":["SA"],"is_corresponding":false,"raw_author_name":"Irfan Ullah Khan","raw_affiliation_strings":["Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia","institution_ids":["https://openalex.org/I76571253"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036961841","display_name":"Nida Aslam","orcid":"https://orcid.org/0000-0002-1619-5733"},"institutions":[{"id":"https://openalex.org/I76571253","display_name":"Imam Abdulrahman Bin Faisal University","ror":"https://ror.org/038cy8j79","country_code":"SA","type":"education","lineage":["https://openalex.org/I76571253"]}],"countries":["SA"],"is_corresponding":false,"raw_author_name":"Nida Aslam","raw_affiliation_strings":["Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia","institution_ids":["https://openalex.org/I76571253"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I76571253"],"apc_list":null,"apc_paid":null,"fwci":1.2361,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.87136284,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"0298","last_page":"0302"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.9995999932289124,"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":0.9995999932289124,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9707000255584717,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"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/random-forest","display_name":"Random forest","score":0.7745085954666138},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7520911693572998},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.7375118732452393},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7124160528182983},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.675884485244751},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.6708714962005615},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.567684531211853},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.5431207418441772},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"Multilayer perceptron","score":0.531622588634491},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.5067198872566223},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.41396015882492065},{"id":"https://openalex.org/keywords/hyperparameter-optimization","display_name":"Hyperparameter optimization","score":0.4135352075099945},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3745681643486023},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.34782588481903076},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.17117127776145935}],"concepts":[{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.7745085954666138},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7520911693572998},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7375118732452393},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7124160528182983},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.675884485244751},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.6708714962005615},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.567684531211853},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.5431207418441772},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.531622588634491},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.5067198872566223},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.41396015882492065},{"id":"https://openalex.org/C10485038","wikidata":"https://www.wikidata.org/wiki/Q48996162","display_name":"Hyperparameter optimization","level":3,"score":0.4135352075099945},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3745681643486023},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.34782588481903076},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.17117127776145935},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/uemcon51285.2020.9298051","is_oa":false,"landing_page_url":"https://doi.org/10.1109/uemcon51285.2020.9298051","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W1979263106","https://openalex.org/W2140190241","https://openalex.org/W2900794383","https://openalex.org/W2954788759","https://openalex.org/W2970092662","https://openalex.org/W2979377734","https://openalex.org/W6767255924"],"related_works":["https://openalex.org/W4389954502","https://openalex.org/W1663203009","https://openalex.org/W2771255398","https://openalex.org/W2930428186","https://openalex.org/W3200027047","https://openalex.org/W3125536479","https://openalex.org/W4214820172","https://openalex.org/W4386984454","https://openalex.org/W3120363735","https://openalex.org/W2394323384"],"abstract_inverted_index":{"Nowadays,":[0],"heart":[1,73],"disease":[2,74],"is":[3],"considered":[4],"as":[5,52],"one":[6],"of":[7,12,28,34,72,115,119],"the":[8,20,26,32,109,113],"most":[9],"significant":[10],"factors":[11],"death.":[13],"Several":[14,79],"attempts":[15],"have":[16],"been":[17,39],"made":[18],"over":[19],"last":[21],"few":[22],"years":[23],"to":[24,69],"automate":[25],"diagnosis":[27],"cardiac":[29],"disease.":[30],"Nevertheless,":[31],"significance":[33],"machine":[35,48],"learning":[36,49],"has":[37],"already":[38],"proved":[40],"from":[41],"literature":[42],"studies.":[43],"In":[44],"our":[45],"study,":[46],"several":[47],"algorithms":[50],"such":[51],"Naive":[53],"Bayes":[54],"(NB),":[55],"Multi-Layer":[56],"Perceptron":[57],"(MLP),":[58],"Random":[59,106],"Forest":[60,107],"(RF)":[61],"and":[62,117],"Decision":[63],"Tree":[64],"(DT)":[65],"will":[66,82,88,97],"be":[67,83,89,98],"compared":[68],"predict":[70],"presence":[71],"using":[75],"UCI":[76],"data":[77],"set.":[78],"preprocessing":[80],"techniques":[81],"applied;":[84],"brute":[85],"force":[86],"technique":[87],"used":[90,99],"for":[91,100],"feature":[92],"selection.":[93],"Grid":[94],"search":[95],"mechanism":[96],"parameter":[101],"optimization.":[102],"Experiments":[103],"showed":[104],"that":[105],"achieved":[108],"highest":[110],"performance":[111],"with":[112],"accuracy":[114],"0.93":[116],"AUC":[118],"0.95.":[120]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}