{"id":"https://openalex.org/W2953605441","doi":"https://doi.org/10.1109/access.2019.2927080","title":"On the Scalability of Machine-Learning Algorithms for Breast Cancer Prediction in Big Data Context","display_name":"On the Scalability of Machine-Learning Algorithms for Breast Cancer Prediction in Big Data Context","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2953605441","doi":"https://doi.org/10.1109/access.2019.2927080","mag":"2953605441"},"language":"en","primary_location":{"id":"doi:10.1109/access.2019.2927080","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2927080","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08755975.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08755975.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5017000950","display_name":"Sara Alghunaim","orcid":"https://orcid.org/0000-0002-8266-6361"},"institutions":[{"id":"https://openalex.org/I1284598098","display_name":"King Abdulaziz City for Science and Technology","ror":"https://ror.org/05tdz6m39","country_code":"SA","type":"facility","lineage":["https://openalex.org/I1284598098"]}],"countries":["SA"],"is_corresponding":false,"raw_author_name":"Sara Alghunaim","raw_affiliation_strings":["National Center for Artificial Intelligence and Big Data Technology, King Abdulaziz City for Science and Technology, Riyadh, Saudi Arabia"],"raw_orcid":"https://orcid.org/0000-0002-8266-6361","affiliations":[{"raw_affiliation_string":"National Center for Artificial Intelligence and Big Data Technology, King Abdulaziz City for Science and Technology, Riyadh, Saudi Arabia","institution_ids":["https://openalex.org/I1284598098"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023986223","display_name":"Heyam H. Al-Baity","orcid":"https://orcid.org/0000-0002-4590-3399"},"institutions":[{"id":"https://openalex.org/I28022161","display_name":"King Saud University","ror":"https://ror.org/02f81g417","country_code":"SA","type":"education","lineage":["https://openalex.org/I28022161"]}],"countries":["SA"],"is_corresponding":false,"raw_author_name":"Heyam H. Al-Baity","raw_affiliation_strings":["IT Department, King Saud University, Riyadh, Saudi Arabia"],"raw_orcid":"https://orcid.org/0000-0002-4590-3399","affiliations":[{"raw_affiliation_string":"IT Department, King Saud University, Riyadh, Saudi Arabia","institution_ids":["https://openalex.org/I28022161"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1750,"currency":"USD","value_usd":1750},"apc_paid":{"value":1750,"currency":"USD","value_usd":1750},"fwci":9.123,"has_fulltext":true,"cited_by_count":98,"citation_normalized_percentile":{"value":0.98137028,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":"7","issue":null,"first_page":"91535","last_page":"91546"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9976000189781189,"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":0.9976000189781189,"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/T10885","display_name":"Gene expression and cancer classification","score":0.9959999918937683,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.9803000092506409,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.7943724989891052},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7758259773254395},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.715404748916626},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.6987379193305969},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.695842444896698},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6716167330741882},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6652343273162842},{"id":"https://openalex.org/keywords/spark","display_name":"SPARK (programming language)","score":0.6175966262817383},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5595053434371948},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.5010249614715576},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.4919397532939911},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.4563586711883545},{"id":"https://openalex.org/keywords/statistical-classification","display_name":"Statistical classification","score":0.4472455084323883},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.44108256697654724},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.13974592089653015}],"concepts":[{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7943724989891052},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7758259773254395},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.715404748916626},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.6987379193305969},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.695842444896698},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6716167330741882},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6652343273162842},{"id":"https://openalex.org/C2781215313","wikidata":"https://www.wikidata.org/wiki/Q3493345","display_name":"SPARK (programming language)","level":2,"score":0.6175966262817383},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5595053434371948},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.5010249614715576},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.4919397532939911},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.4563586711883545},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.4472455084323883},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.44108256697654724},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.13974592089653015},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2019.2927080","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2927080","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08755975.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:15d781dd020e49068052aa3b164548b9","is_oa":true,"landing_page_url":"https://doaj.org/article/15d781dd020e49068052aa3b164548b9","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 7, Pp 91535-91546 (2019)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2019.2927080","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2927080","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08755975.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Good health and well-being","score":0.41999998688697815,"id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321145","display_name":"King Saud University","ror":"https://ror.org/02f81g417"},{"id":"https://openalex.org/F4320335726","display_name":"Deanship of Scientific Research, King Saud University","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2953605441.pdf","grobid_xml":"https://content.openalex.org/works/W2953605441.grobid-xml"},"referenced_works_count":19,"referenced_works":["https://openalex.org/W13034104","https://openalex.org/W273955616","https://openalex.org/W1503398984","https://openalex.org/W1952835952","https://openalex.org/W1984807040","https://openalex.org/W1986407511","https://openalex.org/W2040814250","https://openalex.org/W2069914810","https://openalex.org/W2088338744","https://openalex.org/W2164229713","https://openalex.org/W2230939080","https://openalex.org/W2342284221","https://openalex.org/W2519043672","https://openalex.org/W2615505937","https://openalex.org/W2770426151","https://openalex.org/W2948339884","https://openalex.org/W4236137412","https://openalex.org/W6610017368","https://openalex.org/W6746218878"],"related_works":["https://openalex.org/W4366990902","https://openalex.org/W4317732970","https://openalex.org/W2944292463","https://openalex.org/W4388550696","https://openalex.org/W3014252901","https://openalex.org/W4321636153","https://openalex.org/W4313289487","https://openalex.org/W2188759683","https://openalex.org/W4317376680","https://openalex.org/W3080944905"],"abstract_inverted_index":{"Recent":[0],"advances":[1],"in":[2,43,121,143,159,178,200],"information":[3],"technology":[4],"have":[5],"induced":[6],"an":[7,169],"explosive":[8],"growth":[9],"of":[10,16,29,39,52,64,148,152,161,189],"data,":[11,53],"creating":[12],"a":[13,94,127],"new":[14,27],"era":[15],"big":[17,30,45],"data.":[18,31,190],"Unfortunately,":[19],"traditional":[20],"machine-learning":[21,72],"algorithms":[22,73],"cannot":[23],"cope":[24],"with":[25,134,186,219],"the":[26,37,44,71,135,149,156,196,201,205,211,215,220],"characteristics":[28],"In":[32,96],"this":[33,65,87,97],"paper,":[34,98],"we":[35,89,99,167],"address":[36],"problem":[38],"breast":[40,123],"cancer":[41],"prediction":[42],"data":[46,153],"context.":[47],"We":[48,125],"considered":[49],"two":[50,173],"varieties":[51],"namely,":[54,105],"gene":[55],"expression":[56],"(GE)":[57],"and":[58,84,112,138,140,163,176,214],"DNA":[59],"methylation":[60],"(DM).":[61],"The":[62,191],"objective":[63],"paper":[66],"is":[67],"to":[68,115,145,180],"scale":[69],"up":[70],"that":[74,119,195],"are":[75],"used":[76],"for":[77],"classification":[78,103],"by":[79],"applying":[80],"each":[81],"dataset":[82],"separately":[83],"jointly.":[85],"For":[86],"purpose,":[88],"chose":[90],"Apache":[91],"Spark":[92,202],"as":[93,208],"platform.":[95],"selected":[100],"three":[101,132,150],"different":[102],"algorithms,":[104],"support":[106],"vector":[107],"machine":[108],"(SVM),":[109],"decision":[110],"tree,":[111],"random":[113],"forest,":[114],"create":[116],"nine":[117],"models":[118],"help":[120],"predicting":[122],"cancer.":[124],"conducted":[126],"comprehensive":[128],"comparative":[129],"study":[130],"using":[131],"scenarios":[133],"GE,":[136],"DM,":[137],"GE":[139,221],"DM":[141],"combined,":[142],"order":[144,179],"show":[146,181],"which":[147],"types":[151],"would":[154],"produce":[155],"best":[157],"result":[158],"terms":[160],"accuracy":[162,213],"error":[164,217],"rate.":[165],"Moreover,":[166],"performed":[168],"experimental":[170,192],"comparison":[171],"between":[172],"platforms":[174],"(Spark":[175],"Weka)":[177],"their":[182],"behavior":[183],"when":[184],"dealing":[185],"large":[187],"sets":[188],"results":[193],"showed":[194],"scaled":[197],"SVM":[198],"classifier":[199],"environment":[203],"outperforms":[204],"other":[206],"classifiers,":[207],"it":[209],"achieved":[210],"highest":[212],"lowest":[216],"rate":[218],"dataset.":[222]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":12},{"year":2024,"cited_by_count":13},{"year":2023,"cited_by_count":17},{"year":2022,"cited_by_count":25},{"year":2021,"cited_by_count":14},{"year":2020,"cited_by_count":14},{"year":2019,"cited_by_count":1}],"updated_date":"2026-08-28T12:50:07.497085","created_date":"2025-10-10T00:00:00"}