{"id":"https://openalex.org/W3215649970","doi":"https://doi.org/10.1109/isncc52172.2021.9615727","title":"Interpretable Machine Learning: A Case Study of Healthcare","display_name":"Interpretable Machine Learning: A Case Study of Healthcare","publication_year":2021,"publication_date":"2021-10-31","ids":{"openalex":"https://openalex.org/W3215649970","doi":"https://doi.org/10.1109/isncc52172.2021.9615727","mag":"3215649970"},"language":"en","primary_location":{"id":"doi:10.1109/isncc52172.2021.9615727","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isncc52172.2021.9615727","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Symposium on Networks, Computers and Communications (ISNCC)","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/A5066314235","display_name":"Feyza Y\u0131ld\u0131r\u0131m Okay","orcid":"https://orcid.org/0000-0002-6239-3722"},"institutions":[{"id":"https://openalex.org/I95634034","display_name":"Gazi University","ror":"https://ror.org/054xkpr46","country_code":"TR","type":"education","lineage":["https://openalex.org/I95634034"]}],"countries":["TR"],"is_corresponding":true,"raw_author_name":"Feyza Yildirim Okay","raw_affiliation_strings":["Gazi University, Ankara, Turkey"],"affiliations":[{"raw_affiliation_string":"Gazi University, Ankara, Turkey","institution_ids":["https://openalex.org/I95634034"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100734622","display_name":"Mustafa Y\u0131ld\u0131r\u0131m","orcid":"https://orcid.org/0000-0002-8880-5457"},"institutions":[{"id":"https://openalex.org/I95634034","display_name":"Gazi University","ror":"https://ror.org/054xkpr46","country_code":"TR","type":"education","lineage":["https://openalex.org/I95634034"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Mustafa Yildirim","raw_affiliation_strings":["Gazi University, Ankara, Turkey"],"affiliations":[{"raw_affiliation_string":"Gazi University, Ankara, Turkey","institution_ids":["https://openalex.org/I95634034"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076499033","display_name":"Suat \u00d6zdem\u0131r","orcid":"https://orcid.org/0000-0002-4588-4538"},"institutions":[{"id":"https://openalex.org/I66514158","display_name":"Hacettepe University","ror":"https://ror.org/04kwvgz42","country_code":"TR","type":"education","lineage":["https://openalex.org/I66514158"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Suat Ozdemir","raw_affiliation_strings":["Hacettepe University, Ankara, Turkey"],"affiliations":[{"raw_affiliation_string":"Hacettepe University, Ankara, Turkey","institution_ids":["https://openalex.org/I66514158"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5066314235"],"corresponding_institution_ids":["https://openalex.org/I95634034"],"apc_list":null,"apc_paid":null,"fwci":0.8158,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.78786562,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.9998999834060669,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9864000082015991,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.9789000153541565,"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/interpretability","display_name":"Interpretability","score":0.9123998880386353},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8067894577980042},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.7660305500030518},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7193217873573303},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.6045785546302795},{"id":"https://openalex.org/keywords/transparency","display_name":"Transparency (behavior)","score":0.5890694260597229},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.5312998294830322},{"id":"https://openalex.org/keywords/health-care","display_name":"Health care","score":0.507213830947876},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.46308404207229614},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.43444982171058655},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.1441137194633484}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.9123998880386353},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8067894577980042},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7660305500030518},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7193217873573303},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.6045785546302795},{"id":"https://openalex.org/C2780233690","wikidata":"https://www.wikidata.org/wiki/Q535347","display_name":"Transparency (behavior)","level":2,"score":0.5890694260597229},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.5312998294830322},{"id":"https://openalex.org/C160735492","wikidata":"https://www.wikidata.org/wiki/Q31207","display_name":"Health care","level":2,"score":0.507213830947876},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.46308404207229614},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.43444982171058655},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.1441137194633484},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isncc52172.2021.9615727","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isncc52172.2021.9615727","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Symposium on Networks, Computers and Communications (ISNCC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7799999713897705,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W38301456","https://openalex.org/W1988195734","https://openalex.org/W2026805626","https://openalex.org/W2070493638","https://openalex.org/W2112076978","https://openalex.org/W2282821441","https://openalex.org/W2888487581","https://openalex.org/W2891503716","https://openalex.org/W2946737738","https://openalex.org/W2962122314","https://openalex.org/W2962862931","https://openalex.org/W2963847595","https://openalex.org/W2964303497","https://openalex.org/W2966829450","https://openalex.org/W2996061341","https://openalex.org/W3007549203","https://openalex.org/W3013308250","https://openalex.org/W3039232647","https://openalex.org/W3102363003","https://openalex.org/W3105107900","https://openalex.org/W3108895775","https://openalex.org/W3111336095","https://openalex.org/W3133894893","https://openalex.org/W3200581805","https://openalex.org/W4287864753","https://openalex.org/W4288313203","https://openalex.org/W4402843978","https://openalex.org/W6601578796","https://openalex.org/W6676769703","https://openalex.org/W6737947904","https://openalex.org/W6765558359","https://openalex.org/W6765629856","https://openalex.org/W6785850824","https://openalex.org/W6786431658"],"related_works":["https://openalex.org/W4361806667","https://openalex.org/W2967733078","https://openalex.org/W3204430031","https://openalex.org/W3137904399","https://openalex.org/W4310492845","https://openalex.org/W4386690025","https://openalex.org/W2885778889","https://openalex.org/W2766514146","https://openalex.org/W2885516856","https://openalex.org/W2073883415"],"abstract_inverted_index":{"With":[0],"the":[1,17,86,98,114,131,135,140,149,168,178],"evolution":[2],"of":[3,19,26,44,89,96,101,113,151,180,221],"artificial":[4],"intelligence,":[5],"Machine":[6,73],"Learning":[7,74],"(ML)":[8],"techniques":[9,21,198],"have":[10],"become":[11,23,126],"more":[12],"powerful":[13],"predictors,":[14],"and":[15,107,163,172,183,195],"accordingly,":[16],"use":[18],"ML":[20,48,91,102,197],"has":[22],"a":[24,60,77,82,154],"part":[25],"our":[27],"daily":[28],"life":[29],"in":[30,118],"different":[31],"application":[32],"scenarios":[33,120],"such":[34,121],"as":[35,122,137,139],"disease":[36],"diagnosis,":[37],"movie":[38],"recommendation,":[39],"monitoring":[40],"system,":[41],"or":[42],"detection":[43],"malicious":[45],"attacks.":[46],"Although":[47],"provides":[49,94],"high":[50],"accurate":[51],"predictions,":[52],"it":[53,63,124],"suffers":[54],"from":[55],"opacity.":[56],"By":[57],"behaving":[58],"like":[59],"black":[61],"box":[62],"excluded":[64],"users":[65,109],"about":[66],"how":[67,97],"to":[68,85,104,110,129,147,193],"reach":[69],"particular":[70],"decisions.":[71],"Interpretable":[72],"(IML)":[75],"is":[76,218],"recent":[78],"technology":[79],"that":[80,133,189],"offers":[81],"promising":[83],"solution":[84],"opaqueness":[87],"problem":[88,179],"complex":[90,194],"techniques.":[92],"It":[93],"transparency":[95],"inner":[99],"workings":[100],"lead":[103],"certain":[105],"decisions":[106],"allows":[108],"be":[111],"aware":[112],"decision-making":[115],"process.":[116],"Especially,":[117],"critical":[119],"healthcare,":[123],"may":[125],"extremely":[127],"important":[128],"know":[130],"reasons":[132],"affect":[134],"decision":[136],"well":[138],"result.":[141],"In":[142,158],"this":[143],"study,":[144],"we":[145,160],"aim":[146],"show":[148],"benefits":[150],"IML":[152,165,191],"over":[153],"healthcare":[155],"case":[156],"study.":[157],"experiments,":[159],"employ":[161],"SHAP":[162],"LIME":[164],"models":[166,192],"for":[167,177,209],"Random":[169],"Forest":[170],"(RF)":[171],"Gradient":[173],"Boosting":[174],"(GB)":[175],"algorithms":[176],"diagnosing":[181],"diabetes":[182],"its":[184],"explanations.":[185],"Overall":[186],"results":[187],"exhibit":[188],"applying":[190],"hard-to-interpret":[196],"ensures":[199],"detailed":[200],"interpretability":[201,211],"while":[202],"maintaining":[203],"accuracy.":[204],"We":[205],"also":[206],"perform":[207],"experiments":[208],"local":[210],"by":[212],"focusing":[213],"on":[214],"an":[215],"instance,":[216],"which":[217],"another":[219],"advantage":[220],"IML.":[222]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":4},{"year":2022,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
