{"id":"https://openalex.org/W4388951133","doi":"https://doi.org/10.1109/icccnt56998.2023.10307797","title":"Enhanced extremely boosted neural network (EXBNet) for effective Heart Disease Prediction","display_name":"Enhanced extremely boosted neural network (EXBNet) for effective Heart Disease Prediction","publication_year":2023,"publication_date":"2023-07-06","ids":{"openalex":"https://openalex.org/W4388951133","doi":"https://doi.org/10.1109/icccnt56998.2023.10307797"},"language":"en","primary_location":{"id":"doi:10.1109/icccnt56998.2023.10307797","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icccnt56998.2023.10307797","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)","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/A5093327636","display_name":"Bhagya Laxmi S M","orcid":null},"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":"Bhagya Laxmi S M","raw_affiliation_strings":["REVA University,School of Computer Science and Engineering,Bengaluru,India","School of Computer Science and Engineering, REVA University, Bengaluru, India"],"affiliations":[{"raw_affiliation_string":"REVA University,School of Computer Science and Engineering,Bengaluru,India","institution_ids":["https://openalex.org/I83737708"]},{"raw_affiliation_string":"School of Computer Science and Engineering, REVA University, Bengaluru, India","institution_ids":["https://openalex.org/I83737708"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5114102185","display_name":"Kavitha J.C","orcid":null},"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":"Kavitha J.C","raw_affiliation_strings":["REVA University,School of Computer Science and Engineering,Bengaluru,India","School of Computer Science and Engineering, REVA University, Bengaluru, India"],"affiliations":[{"raw_affiliation_string":"REVA University,School of Computer Science and Engineering,Bengaluru,India","institution_ids":["https://openalex.org/I83737708"]},{"raw_affiliation_string":"School of Computer Science and Engineering, REVA University, Bengaluru, India","institution_ids":["https://openalex.org/I83737708"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5093327636"],"corresponding_institution_ids":["https://openalex.org/I83737708"],"apc_list":null,"apc_paid":null,"fwci":0.3752,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.72743804,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"22","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":0.9983000159263611,"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":0.9983000159263611,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.9713000059127808,"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/T11021","display_name":"ECG Monitoring and Analysis","score":0.965399980545044,"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/computer-science","display_name":"Computer science","score":0.7955355644226074},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.7745082378387451},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.646033763885498},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.560501217842102},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.488321989774704},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.48157620429992676},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.47779709100723267},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.46925994753837585},{"id":"https://openalex.org/keywords/gradient-descent","display_name":"Gradient descent","score":0.46681615710258484},{"id":"https://openalex.org/keywords/stochastic-gradient-descent","display_name":"Stochastic gradient descent","score":0.43529099225997925},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.42506468296051025}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7955355644226074},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.7745082378387451},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.646033763885498},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.560501217842102},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.488321989774704},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.48157620429992676},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.47779709100723267},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.46925994753837585},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.46681615710258484},{"id":"https://openalex.org/C206688291","wikidata":"https://www.wikidata.org/wiki/Q7617819","display_name":"Stochastic gradient descent","level":3,"score":0.43529099225997925},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.42506468296051025},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icccnt56998.2023.10307797","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icccnt56998.2023.10307797","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.800000011920929,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1965587486","https://openalex.org/W1970805379","https://openalex.org/W2062302861","https://openalex.org/W2078590217","https://openalex.org/W2270635011","https://openalex.org/W2397911846","https://openalex.org/W2427094903","https://openalex.org/W2735054537","https://openalex.org/W2790827964","https://openalex.org/W2803098482","https://openalex.org/W2885492729","https://openalex.org/W2920805549","https://openalex.org/W2949767632","https://openalex.org/W2985962305","https://openalex.org/W2993696341","https://openalex.org/W4213311745","https://openalex.org/W6685812147","https://openalex.org/W6694064012"],"related_works":["https://openalex.org/W4206903459","https://openalex.org/W2754816816","https://openalex.org/W4366280654","https://openalex.org/W3160167280","https://openalex.org/W4231621013","https://openalex.org/W4362706668","https://openalex.org/W3008318776","https://openalex.org/W1977633006","https://openalex.org/W2041416246","https://openalex.org/W3020853991"],"abstract_inverted_index":{"Cardiovascular":[0],"disease":[1,88],"is":[2,23,108,116,128,163,200],"considered":[3],"to":[4,25,35,77,118],"be":[5],"one":[6],"of":[7,11,54,59,111,123,160,195,205],"the":[8,14,37,57,120,149,176,214],"major":[9],"causes":[10],"deaths":[12],"around":[13],"world.":[15],"Hence,":[16],"a":[17,47,109],"reliable,":[18],"accurate,":[19],"and":[20,43,65,139,172,182,208],"efficient":[21],"approach":[22],"needed":[24],"diagnose":[26],"such":[27],"diseases":[28],"at":[29],"an":[30,85,91,129,189,193],"early":[31],"stage":[32],"so":[33],"as":[34],"start":[36],"relevant":[38],"treatment":[39],"procedure.":[40],"Machine":[41],"learning":[42],"Neural":[44,62,67,142],"network":[45,96],"plays":[46],"critical":[48],"role":[49],"in":[50,56,74],"processing":[51],"massive":[52],"amount":[53],"data":[55,150],"field":[58],"healthcare.":[60],"Artificial":[61],"Network":[63,68],"(ANN)":[64],"Deep":[66],"(DNN)":[69],"techniques":[70],"have":[71],"been":[72,153,211],"used":[73,117],"earlier":[75],"research":[76,186],"predict":[78],"heart":[79,87],"diseases.":[80],"The":[81,103,144,158,185,197],"proposed":[82,104,161,198,215],"methodology":[83,162,187,199],"presents":[84],"effective":[86],"prediction":[89],"using":[90,155,169,175],"Enhanced":[92,106],"Extremely":[93],"boosted":[94],"neural":[95],"(EXBNet)":[97],"that":[98,132,151,213],"can":[99],"assist":[100],"medical":[101],"professionals.":[102],"method,":[105],"XBNET":[107,112,127],"variation":[110],"where":[113],"Gini":[114],"Index":[115],"compute":[119],"feature":[121],"importance":[122],"Gradient":[124,135],"Descent":[125],"trees.":[126],"ensemble":[130],"model":[131],"combines":[133],"both":[134],"Boost":[136],"tree-based":[137],"models":[138],"Feed":[140],"Forward":[141],"networks.":[143],"experiments":[145],"were":[146],"conducted":[147],"on":[148],"has":[152,210],"collected":[154],"Kaggle":[156],"datasets.":[157],"performance":[159,177,191],"compared":[164,201],"against":[165],"various":[166,203],"state-of-art":[167],"methods":[168],"different":[170],"classifiers":[171],"are":[173],"evaluated":[174],"metrics":[178],"Accuracy,":[179],"Recall,":[180],"Precision,":[181],"F1":[183],"score.":[184],"provides":[188],"enhanced":[190],"with":[192,202],"accuracy":[194],"100%.":[196],"state":[204],"art":[206],"algorithms":[207],"it":[209],"proved":[212],"method":[216],"outperforms":[217],"other":[218],"algorithms.":[219]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-12-23T23:11:35.936235","created_date":"2025-10-10T00:00:00"}
