{"id":"https://openalex.org/W4414058750","doi":"https://doi.org/10.1007/s44230-025-00111-8","title":"Interpretable Machine Learning Approach for Breast Cancer Classification","display_name":"Interpretable Machine Learning Approach for Breast Cancer Classification","publication_year":2025,"publication_date":"2025-09-08","ids":{"openalex":"https://openalex.org/W4414058750","doi":"https://doi.org/10.1007/s44230-025-00111-8"},"language":"en","primary_location":{"id":"doi:10.1007/s44230-025-00111-8","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s44230-025-00111-8","pdf_url":"https://link.springer.com/content/pdf/10.1007/s44230-025-00111-8.pdf","source":{"id":"https://openalex.org/S4210207486","display_name":"Human-Centric Intelligent Systems","issn_l":"2667-1336","issn":["2667-1336"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Human-Centric Intelligent Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://link.springer.com/content/pdf/10.1007/s44230-025-00111-8.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5119567890","display_name":"Ahmad Tijjani Garba","orcid":null},"institutions":[{"id":"https://openalex.org/I919958821","display_name":"Bayero University Kano","ror":"https://ror.org/049pzty39","country_code":"NG","type":"education","lineage":["https://openalex.org/I919958821"]}],"countries":["NG"],"is_corresponding":true,"raw_author_name":"Ahmad Tijjani Garba","raw_affiliation_strings":["Department of Information Technology, Faculty of Computing, Bayero University, Kano, Nigeria"],"affiliations":[{"raw_affiliation_string":"Department of Information Technology, Faculty of Computing, Bayero University, Kano, Nigeria","institution_ids":["https://openalex.org/I919958821"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016190308","display_name":"Hisham Hamza","orcid":null},"institutions":[{"id":"https://openalex.org/I919958821","display_name":"Bayero University Kano","ror":"https://ror.org/049pzty39","country_code":"NG","type":"education","lineage":["https://openalex.org/I919958821"]}],"countries":["NG"],"is_corresponding":false,"raw_author_name":"Hafsah Shuaibu Hamza","raw_affiliation_strings":["Department of Information Technology, Faculty of Computing, Bayero University, Kano, Nigeria"],"affiliations":[{"raw_affiliation_string":"Department of Information Technology, Faculty of Computing, Bayero University, Kano, Nigeria","institution_ids":["https://openalex.org/I919958821"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5119567890"],"corresponding_institution_ids":["https://openalex.org/I919958821"],"apc_list":null,"apc_paid":null,"fwci":9.0399,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":{"value":0.97593132,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":"5","issue":"3","first_page":"308","last_page":"322"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9994999766349792,"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/T10862","display_name":"AI in cancer detection","score":0.9994999766349792,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9966999888420105,"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"}},{"id":"https://openalex.org/T10556","display_name":"Global Cancer Incidence and Screening","score":0.9718000292778015,"subfield":{"id":"https://openalex.org/subfields/2730","display_name":"Oncology"},"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":0.5644000172615051},{"id":"https://openalex.org/keywords/receiver-operating-characteristic","display_name":"Receiver operating characteristic","score":0.5486000180244446},{"id":"https://openalex.org/keywords/breast-cancer","display_name":"Breast cancer","score":0.5357000231742859},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.5091000199317932},{"id":"https://openalex.org/keywords/transparency","display_name":"Transparency (behavior)","score":0.4880000054836273},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.35530000925064087},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.3483000099658966},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.34389999508857727}],"concepts":[{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.8069000244140625},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7878999710083008},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5644000172615051},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5561000108718872},{"id":"https://openalex.org/C58471807","wikidata":"https://www.wikidata.org/wiki/Q327120","display_name":"Receiver operating characteristic","level":2,"score":0.5486000180244446},{"id":"https://openalex.org/C530470458","wikidata":"https://www.wikidata.org/wiki/Q128581","display_name":"Breast cancer","level":3,"score":0.5357000231742859},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.5091000199317932},{"id":"https://openalex.org/C2780233690","wikidata":"https://www.wikidata.org/wiki/Q535347","display_name":"Transparency (behavior)","level":2,"score":0.4880000054836273},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.35530000925064087},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3483000099658966},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.34389999508857727},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.328900009393692},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.3118000030517578},{"id":"https://openalex.org/C100660578","wikidata":"https://www.wikidata.org/wiki/Q18733","display_name":"Recall","level":2,"score":0.29490000009536743},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.2833999991416931},{"id":"https://openalex.org/C121608353","wikidata":"https://www.wikidata.org/wiki/Q12078","display_name":"Cancer","level":2,"score":0.27730000019073486},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.27129998803138733},{"id":"https://openalex.org/C2779974597","wikidata":"https://www.wikidata.org/wiki/Q28448986","display_name":"Clinical Practice","level":2,"score":0.2635999917984009},{"id":"https://openalex.org/C2780472235","wikidata":"https://www.wikidata.org/wiki/Q324634","display_name":"Mammography","level":4,"score":0.26010000705718994},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.2540999948978424},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2500999867916107}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1007/s44230-025-00111-8","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s44230-025-00111-8","pdf_url":"https://link.springer.com/content/pdf/10.1007/s44230-025-00111-8.pdf","source":{"id":"https://openalex.org/S4210207486","display_name":"Human-Centric Intelligent Systems","issn_l":"2667-1336","issn":["2667-1336"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Human-Centric Intelligent Systems","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:17a57bea3df649c7adabc86548fc4df6","is_oa":true,"landing_page_url":"https://doaj.org/article/17a57bea3df649c7adabc86548fc4df6","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Human-Centric Intelligent Systems, Vol 5, Iss 3, Pp 308-322 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1007/s44230-025-00111-8","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s44230-025-00111-8","pdf_url":"https://link.springer.com/content/pdf/10.1007/s44230-025-00111-8.pdf","source":{"id":"https://openalex.org/S4210207486","display_name":"Human-Centric Intelligent Systems","issn_l":"2667-1336","issn":["2667-1336"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Human-Centric Intelligent Systems","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4414058750.pdf","grobid_xml":"https://content.openalex.org/works/W4414058750.grobid-xml"},"referenced_works_count":17,"referenced_works":["https://openalex.org/W2806359329","https://openalex.org/W3121563664","https://openalex.org/W3134362176","https://openalex.org/W3157455600","https://openalex.org/W3163101315","https://openalex.org/W3172138850","https://openalex.org/W3197078391","https://openalex.org/W4213094779","https://openalex.org/W4283512560","https://openalex.org/W4283786398","https://openalex.org/W4310333337","https://openalex.org/W4386139031","https://openalex.org/W4389262417","https://openalex.org/W4390637980","https://openalex.org/W4392462303","https://openalex.org/W4400617083","https://openalex.org/W4404688382"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4387369504","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296","https://openalex.org/W4364306694","https://openalex.org/W4312192474"],"abstract_inverted_index":{"Abstract":[0],"Breast":[1,28,33],"cancer":[2],"is":[3],"a":[4,14],"serious":[5],"global":[6],"health":[7],"challenge,":[8],"calling":[9],"for":[10,22,101],"the":[11,40,50,90,107,113],"invention":[12],"of":[13,106,115,135,150],"reliable":[15],"and":[16,36,68,78,81,87,132,140,152,171,186,206],"interpretable":[17,187],"machine":[18],"learning":[19,52,208],"(ML)":[20,53],"models":[21,55,147,209],"its":[23],"early":[24],"detection.":[25],"The":[26,121,154,175],"Wisconsin":[27,31],"Cancer":[29,34],"(WBC),":[30,139,170],"Diagnostic":[32],"(WDBC),":[35,137],"Coimbra":[37],"datasets":[38,44,185,205],"are":[39,57],"three":[41],"publicly":[42],"available":[43],"that":[45,157],"were":[46,96,161],"used":[47],"to":[48,189,210],"train":[49],"four-machine":[51],"classification":[54,212],"which":[56,129],"compared":[58],"in":[59,148,194],"this":[60],"study:":[61],"Logistic":[62],"Regression,":[63],"Decision":[64],"Trees,":[65],"Random":[66],"Forest,":[67],"CatBoost.":[69],"These":[70],"models\u2019":[71],"computational":[72],"efficiency":[73,151],"was":[74,110,124],"measured":[75],"by":[76,163,182],"fit":[77],"test":[79],"times,":[80],"their":[82],"precision,":[83],"recall,":[84],"accuracy,":[85],"F1-score,":[86],"area":[88],"under":[89],"receiver":[91],"operating":[92],"characteristic":[93],"curve":[94],"(AUC-ROC)":[95],"evaluated.":[97],"To":[98],"promote":[99],"transparency":[100],"clinical":[102,159],"adoption,":[103],"feature-level":[104],"analysis":[105],"model\u2019s":[108],"predictions":[109],"captured":[111],"through":[112],"use":[114],"Local":[116],"Interpretable":[117],"Model-Agnostic":[118],"Explanations":[119],"(LIME).":[120],"highest":[122],"accuracy":[123],"achieved":[125],"using":[126],"logistic":[127],"regression,":[128],"recorded":[130],"precision":[131],"recall":[133],"values":[134],"0.97/0.95":[136],"0.95/0.92":[138],"0.85/0.78":[141],"(Coimbra),":[142,167],"respectively,":[143],"thereby":[144],"exceeding":[145],"other":[146],"terms":[149],"consistency.":[153],"Key":[155],"factors":[156],"matched":[158],"expectations":[160],"identified":[162],"LIME,":[164],"including":[165],"BMI":[166],"clump":[168],"thickness":[169],"radius":[172],"mean":[173],"(WDBC).":[174],"present":[176],"research":[177,198],"builds":[178],"on":[179],"previous":[180],"work":[181],"combining":[183],"various":[184],"methodologies":[188],"address":[190],"ML":[191],"black-box":[192],"challenges":[193],"medical":[195,204],"diagnostics.":[196],"Future":[197],"should":[199],"look":[200],"into":[201],"larger,":[202],"multi-layered":[203],"deep":[207],"enhance":[211],"accuracy.":[213]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":1}],"updated_date":"2026-04-17T18:11:37.981687","created_date":"2025-10-10T00:00:00"}
