{"id":"https://openalex.org/W3200985945","doi":"https://doi.org/10.1109/ijcnn52387.2021.9533512","title":"Gender-based approach to estimate the human body fat percentage using Machine Learning","display_name":"Gender-based approach to estimate the human body fat percentage using Machine Learning","publication_year":2021,"publication_date":"2021-07-18","ids":{"openalex":"https://openalex.org/W3200985945","doi":"https://doi.org/10.1109/ijcnn52387.2021.9533512","mag":"3200985945"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn52387.2021.9533512","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9533512","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Joint Conference on Neural Networks (IJCNN)","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/A5011654947","display_name":"Shara S. A. Alves","orcid":"https://orcid.org/0000-0003-3996-4555"},"institutions":[{"id":"https://openalex.org/I243754102","display_name":"Universidade Federal do Cear\u00e1","ror":"https://ror.org/03srtnf24","country_code":"BR","type":"education","lineage":["https://openalex.org/I243754102"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Shara S.A. Alves","raw_affiliation_strings":["Programa de P&#x00F3;s-Gradua&#x00E7;&#x00E3;o em Engenharia de Teleinform&#x00E1;tica (PPGETI), Universidade Federal do Cear&#x00E1;,Fortaleza,Cear\u00e1,Brazil"],"affiliations":[{"raw_affiliation_string":"Programa de P&#x00F3;s-Gradua&#x00E7;&#x00E3;o em Engenharia de Teleinform&#x00E1;tica (PPGETI), Universidade Federal do Cear&#x00E1;,Fortaleza,Cear\u00e1,Brazil","institution_ids":["https://openalex.org/I243754102"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083234811","display_name":"Elene Firmeza Ohata","orcid":"https://orcid.org/0000-0001-8386-2455"},"institutions":[{"id":"https://openalex.org/I243754102","display_name":"Universidade Federal do Cear\u00e1","ror":"https://ror.org/03srtnf24","country_code":"BR","type":"education","lineage":["https://openalex.org/I243754102"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Elene F. Ohata","raw_affiliation_strings":["Programa de P&#x00F3;s-Gradua&#x00E7;&#x00E3;o em Engenharia de Teleinform&#x00E1;tica (PPGETI), Universidade Federal do Cear&#x00E1;,Fortaleza,Cear\u00e1,Brazil"],"affiliations":[{"raw_affiliation_string":"Programa de P&#x00F3;s-Gradua&#x00E7;&#x00E3;o em Engenharia de Teleinform&#x00E1;tica (PPGETI), Universidade Federal do Cear&#x00E1;,Fortaleza,Cear\u00e1,Brazil","institution_ids":["https://openalex.org/I243754102"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065545744","display_name":"Navar Medeiros M. Nascimento","orcid":"https://orcid.org/0000-0001-8803-6793"},"institutions":[{"id":"https://openalex.org/I243754102","display_name":"Universidade Federal do Cear\u00e1","ror":"https://ror.org/03srtnf24","country_code":"BR","type":"education","lineage":["https://openalex.org/I243754102"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Navar M.M. Nascimento","raw_affiliation_strings":["Programa de P&#x00F3;s-Gradua&#x00E7;&#x00E3;o em Engenharia de Teleinform&#x00E1;tica (PPGETI), Universidade Federal do Cear&#x00E1;,Fortaleza,Cear\u00e1,Brazil"],"affiliations":[{"raw_affiliation_string":"Programa de P&#x00F3;s-Gradua&#x00E7;&#x00E3;o em Engenharia de Teleinform&#x00E1;tica (PPGETI), Universidade Federal do Cear&#x00E1;,Fortaleza,Cear\u00e1,Brazil","institution_ids":["https://openalex.org/I243754102"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104000627","display_name":"Jo\u00e3o Wellington M. Souza","orcid":null},"institutions":[{"id":"https://openalex.org/I243754102","display_name":"Universidade Federal do Cear\u00e1","ror":"https://ror.org/03srtnf24","country_code":"BR","type":"education","lineage":["https://openalex.org/I243754102"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Joao W. M. De Souza","raw_affiliation_strings":["Programa de P&#x00F3;s-Gradua&#x00E7;&#x00E3;o em Engenharia de Teleinform&#x00E1;tica (PPGETI), Universidade Federal do Cear&#x00E1;,Fortaleza,Cear\u00e1,Brazil"],"affiliations":[{"raw_affiliation_string":"Programa de P&#x00F3;s-Gradua&#x00E7;&#x00E3;o em Engenharia de Teleinform&#x00E1;tica (PPGETI), Universidade Federal do Cear&#x00E1;,Fortaleza,Cear\u00e1,Brazil","institution_ids":["https://openalex.org/I243754102"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048140802","display_name":"Gabriel B. Holanda","orcid":"https://orcid.org/0000-0001-7850-8136"},"institutions":[{"id":"https://openalex.org/I3018325552","display_name":"Instituto Federal de Educa\u00e7\u00e3o, Ci\u00eancia e Tecnologia do Cear\u00e1","ror":"https://ror.org/02225fd27","country_code":"BR","type":"government","lineage":["https://openalex.org/I3018325552"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Gabriel B. Holanda","raw_affiliation_strings":["Instituto Federal do Cear&#x00E1;,Laboratory of Image Processing, Signs and Applied Computer (LAPISCO),Fortaleza,Cear\u00e1,Brazil"],"affiliations":[{"raw_affiliation_string":"Instituto Federal do Cear&#x00E1;,Laboratory of Image Processing, Signs and Applied Computer (LAPISCO),Fortaleza,Cear\u00e1,Brazil","institution_ids":["https://openalex.org/I3018325552"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009939466","display_name":"Luiz Lannes Loureiro","orcid":"https://orcid.org/0000-0001-8220-4119"},"institutions":[{"id":"https://openalex.org/I243754102","display_name":"Universidade Federal do Cear\u00e1","ror":"https://ror.org/03srtnf24","country_code":"BR","type":"education","lineage":["https://openalex.org/I243754102"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Luiz Lannes Loureiro","raw_affiliation_strings":["Programa de P&#x00F3;s-Gradua&#x00E7;&#x00E3;o em Engenharia de Teleinform&#x00E1;tica (PPGETI), Universidade Federal do Cear&#x00E1;,Fortaleza,Cear\u00e1,Brazil"],"affiliations":[{"raw_affiliation_string":"Programa de P&#x00F3;s-Gradua&#x00E7;&#x00E3;o em Engenharia de Teleinform&#x00E1;tica (PPGETI), Universidade Federal do Cear&#x00E1;,Fortaleza,Cear\u00e1,Brazil","institution_ids":["https://openalex.org/I243754102"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5049651824","display_name":"Pedro P. Rebou\u00e7as Filho","orcid":"https://orcid.org/0000-0002-1878-5489"},"institutions":[{"id":"https://openalex.org/I3018325552","display_name":"Instituto Federal de Educa\u00e7\u00e3o, Ci\u00eancia e Tecnologia do Cear\u00e1","ror":"https://ror.org/02225fd27","country_code":"BR","type":"government","lineage":["https://openalex.org/I3018325552"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Pedro Pedrosa Reboucas Filho","raw_affiliation_strings":["Instituto Federal do Cear&#x00E1;,Laboratory of Image Processing, Signs and Applied Computer (LAPISCO),Fortaleza,Cear\u00e1,Brazil"],"affiliations":[{"raw_affiliation_string":"Instituto Federal do Cear&#x00E1;,Laboratory of Image Processing, Signs and Applied Computer (LAPISCO),Fortaleza,Cear\u00e1,Brazil","institution_ids":["https://openalex.org/I3018325552"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5011654947"],"corresponding_institution_ids":["https://openalex.org/I243754102"],"apc_list":null,"apc_paid":null,"fwci":0.5988,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.66666667,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"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/T12279","display_name":"Body Composition Measurement Techniques","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2737","display_name":"Physiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T12279","display_name":"Body Composition Measurement Techniques","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2737","display_name":"Physiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10866","display_name":"Nutritional Studies and Diet","score":0.9259999990463257,"subfield":{"id":"https://openalex.org/subfields/2739","display_name":"Public Health, Environmental and Occupational Health"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11109","display_name":"Thermoregulation and physiological responses","score":0.9010000228881836,"subfield":{"id":"https://openalex.org/subfields/2737","display_name":"Physiology"},"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/body-fat-percentage","display_name":"Body fat percentage","score":0.7224645018577576},{"id":"https://openalex.org/keywords/anthropometry","display_name":"Anthropometry","score":0.6970483064651489},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.6066586971282959},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.5839903950691223},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.5421792268753052},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.5309877991676331},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.5296565890312195},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5065468549728394},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.5060457587242126},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5019493103027344},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46388038992881775},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4575795531272888},{"id":"https://openalex.org/keywords/body-mass-index","display_name":"Body mass index","score":0.4220644533634186},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.36511287093162537},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.31864747405052185},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.17396140098571777}],"concepts":[{"id":"https://openalex.org/C2780005051","wikidata":"https://www.wikidata.org/wiki/Q797258","display_name":"Body fat percentage","level":3,"score":0.7224645018577576},{"id":"https://openalex.org/C61427482","wikidata":"https://www.wikidata.org/wiki/Q6656244","display_name":"Anthropometry","level":2,"score":0.6970483064651489},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.6066586971282959},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.5839903950691223},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5421792268753052},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.5309877991676331},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.5296565890312195},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5065468549728394},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.5060457587242126},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5019493103027344},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46388038992881775},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4575795531272888},{"id":"https://openalex.org/C2780221984","wikidata":"https://www.wikidata.org/wiki/Q131191","display_name":"Body mass index","level":2,"score":0.4220644533634186},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.36511287093162537},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.31864747405052185},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.17396140098571777}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn52387.2021.9533512","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9533512","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7900000214576721,"id":"https://metadata.un.org/sdg/2","display_name":"Zero hunger"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321091","display_name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior","ror":"https://ror.org/00x0ma614"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W178894351","https://openalex.org/W1500743285","https://openalex.org/W1569512666","https://openalex.org/W1596717185","https://openalex.org/W1966147511","https://openalex.org/W1972438272","https://openalex.org/W2051577174","https://openalex.org/W2092903383","https://openalex.org/W2097662925","https://openalex.org/W2112160795","https://openalex.org/W2136910460","https://openalex.org/W2137226992","https://openalex.org/W2149706766","https://openalex.org/W2214790450","https://openalex.org/W2395724056","https://openalex.org/W2612603124","https://openalex.org/W2737328338","https://openalex.org/W2911964244","https://openalex.org/W3045066578","https://openalex.org/W3087552978","https://openalex.org/W3090535222","https://openalex.org/W4236137412","https://openalex.org/W4399647672","https://openalex.org/W6607285077","https://openalex.org/W6680532697","https://openalex.org/W6869608176"],"related_works":["https://openalex.org/W10980763","https://openalex.org/W6310906","https://openalex.org/W6229082","https://openalex.org/W4081608","https://openalex.org/W11023528","https://openalex.org/W8444177","https://openalex.org/W868042","https://openalex.org/W11297145","https://openalex.org/W8248617","https://openalex.org/W7465187"],"abstract_inverted_index":{"Keeping":[0],"a":[1,10,29,122,141,157,221],"certain":[2],"balance":[3],"of":[4,19,74,118,146,159,161],"body":[5,57,77,91,110,133,217],"fat":[6,58,78,92,111,134,218],"is":[7,16,25,47,63,80,96,121],"essential":[8],"for":[9,33],"healthy":[11],"life,":[12],"and":[13,40,51,65,131,151,179,205,225,233,237],"proper":[14],"nutrition":[15],"fundamental.":[17],"One":[18],"the":[20,48,56,76,85,127,132,191,216,230,242],"most":[21,49],"worrying":[22],"malnutrition":[23],"problems":[24],"obesity,":[26],"which":[27,163],"plays":[28],"significant":[30],"risk":[31],"factor":[32],"chronic":[34],"diseases":[35],"like":[36],"cardiovascular":[37],"diseases,":[38],"diabetes,":[39],"cancer.":[41],"The":[42,143,185],"Dual-energy":[43],"X-ray":[44],"absorp-tiometry":[45],"(DXA)":[46],"accurate":[50],"automatic":[52],"method":[53,62],"that":[54,89],"returns":[55],"percentage;":[59],"however,":[60],"this":[61,99,119],"expensive":[64],"not":[66],"easily":[67],"found":[68],"at":[69],"clinics.":[70],"A":[71],"lower-cost":[72],"way":[73],"estimating":[75,90],"percentage":[79,93,112,135,219],"through":[81],"anthropometric":[82,129],"measures.":[83],"However,":[84],"literature":[86],"has":[87],"shown":[88],"on":[94,229,241],"women":[95],"challenging.":[97],"In":[98],"work,":[100],"we":[101],"propose":[102],"an":[103],"approach":[104,213],"specialized":[105],"in":[106],"gender":[107],"to":[108],"estimate":[109],"using":[113],"machine":[114],"learning.":[115],"Another":[116],"contribution":[117],"work":[120],"dataset,":[123],"BodyFat-163":[124],"(BF-163),":[125],"containing":[126],"12":[128],"measures":[130],"from":[136],"DXA":[137],"exams":[138],"collected":[139],"by":[140],"specialist.":[142],"dataset":[144],"consists":[145],"163":[147],"individuals":[148],"(84":[149],"males":[150],"79":[152],"females).":[153],"Our":[154,211],"experiments":[155],"involved":[156],"variety":[158],"methods":[160],"regression,":[162],"includes":[164],"Random":[165],"Forest":[166],"Regression,":[167,175,178],"Extreme":[168],"Gradient":[169],"Boosting,":[170],"Decision":[171],"Tree,":[172],"Support":[173,182],"Vector":[174,183],"Multilayer":[176],"Perceptron":[177],"Least":[180],"Square":[181,199],"Regression.":[184],"experiment":[186],"results":[187],"were":[188],"evaluated":[189],"with":[190],"metrics":[192],"Mean":[193,198,201],"Absolute":[194],"Error":[195],"(MAE),":[196],"Root":[197],"Error,":[200,204],"Squared":[202],"Logarithmic":[203],"R":[206],"<sup":[207],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[208],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">2</sup>":[209],"score.":[210],"gender-based":[212],"successfully":[214],"estimates":[215],"achieving":[220],"MAE":[222,234],"=":[223,227,235,239],"2.756,":[224],"R2":[226,238],"0.68":[228],"male":[231],"set":[232],"3.869,":[236],"0.69":[240],"female":[243],"set.":[244]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":3},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-03-06T13:50:29.536080","created_date":"2025-10-10T00:00:00"}
