{"id":"https://openalex.org/W4313000268","doi":"https://doi.org/10.1109/access.2022.3212067","title":"PE_DIM: An Efficient Probabilistic Ensemble Classification Algorithm for Diabetes Handling Class Imbalance Missing Values","display_name":"PE_DIM: An Efficient Probabilistic Ensemble Classification Algorithm for Diabetes Handling Class Imbalance Missing Values","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4313000268","doi":"https://doi.org/10.1109/access.2022.3212067"},"language":"en","primary_location":{"id":"doi:10.1109/access.2022.3212067","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3212067","pdf_url":null,"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://doi.org/10.1109/access.2022.3212067","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5051586546","display_name":"Liyan Jia","orcid":"https://orcid.org/0000-0003-1107-1480"},"institutions":[{"id":"https://openalex.org/I43313876","display_name":"Dalian Maritime University","ror":"https://ror.org/002b7nr53","country_code":"CN","type":"education","lineage":["https://openalex.org/I43313876"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Liyan Jia","raw_affiliation_strings":["College of Science, Dalian Maritime University, Dalian, China"],"affiliations":[{"raw_affiliation_string":"College of Science, Dalian Maritime University, Dalian, China","institution_ids":["https://openalex.org/I43313876"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017523518","display_name":"Zhiping Wang","orcid":"https://orcid.org/0000-0003-4985-9003"},"institutions":[{"id":"https://openalex.org/I43313876","display_name":"Dalian Maritime University","ror":"https://ror.org/002b7nr53","country_code":"CN","type":"education","lineage":["https://openalex.org/I43313876"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiping Wang","raw_affiliation_strings":["College of Science, Dalian Maritime University, Dalian, China"],"affiliations":[{"raw_affiliation_string":"College of Science, Dalian Maritime University, Dalian, China","institution_ids":["https://openalex.org/I43313876"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058987059","display_name":"Siqi Lv","orcid":"https://orcid.org/0000-0003-0925-3699"},"institutions":[{"id":"https://openalex.org/I43313876","display_name":"Dalian Maritime University","ror":"https://ror.org/002b7nr53","country_code":"CN","type":"education","lineage":["https://openalex.org/I43313876"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Siqi Lv","raw_affiliation_strings":["College of Science, Dalian Maritime University, Dalian, China"],"affiliations":[{"raw_affiliation_string":"College of Science, Dalian Maritime University, Dalian, China","institution_ids":["https://openalex.org/I43313876"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036807364","display_name":"Zhaohui Xu","orcid":"https://orcid.org/0000-0003-1429-3281"},"institutions":[{"id":"https://openalex.org/I4210140813","display_name":"First Affiliated Hospital of Dalian Medical University","ror":"https://ror.org/055w74b96","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210140813"]},{"id":"https://openalex.org/I191996457","display_name":"Dalian Medical University","ror":"https://ror.org/04c8eg608","country_code":"CN","type":"education","lineage":["https://openalex.org/I191996457"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhaohui Xu","raw_affiliation_strings":["Clinical Laboratory Department, The First Affiliated Hospital of Dalian Medical University, Dalian, China"],"affiliations":[{"raw_affiliation_string":"Clinical Laboratory Department, The First Affiliated Hospital of Dalian Medical University, Dalian, China","institution_ids":["https://openalex.org/I191996457","https://openalex.org/I4210140813"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5051586546"],"corresponding_institution_ids":["https://openalex.org/I43313876"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":5.6537,"has_fulltext":false,"cited_by_count":21,"citation_normalized_percentile":{"value":0.96216753,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"10","issue":null,"first_page":"107459","last_page":"107476"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.9998999834060669,"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":0.9998999834060669,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9907000064849854,"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":0.972000002861023,"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/missing-data","display_name":"Missing data","score":0.6727275848388672},{"id":"https://openalex.org/keywords/probabilistic-classification","display_name":"Probabilistic classification","score":0.6423783302307129},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.6354535222053528},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5579880475997925},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.546561598777771},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.517551600933075},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.48833033442497253},{"id":"https://openalex.org/keywords/statistical-classification","display_name":"Statistical classification","score":0.4794539213180542},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.4683014452457428},{"id":"https://openalex.org/keywords/brier-score","display_name":"Brier score","score":0.46600279211997986},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.4648553133010864},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.44998690485954285},{"id":"https://openalex.org/keywords/receiver-operating-characteristic","display_name":"Receiver operating characteristic","score":0.4383629858493805},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4066488742828369},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3741946816444397},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3508091866970062},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.29920756816864014}],"concepts":[{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.6727275848388672},{"id":"https://openalex.org/C189119545","wikidata":"https://www.wikidata.org/wiki/Q5128022","display_name":"Probabilistic classification","level":4,"score":0.6423783302307129},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.6354535222053528},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5579880475997925},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.546561598777771},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.517551600933075},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.48833033442497253},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.4794539213180542},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.4683014452457428},{"id":"https://openalex.org/C35405484","wikidata":"https://www.wikidata.org/wiki/Q4967066","display_name":"Brier score","level":2,"score":0.46600279211997986},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.4648553133010864},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.44998690485954285},{"id":"https://openalex.org/C58471807","wikidata":"https://www.wikidata.org/wiki/Q327120","display_name":"Receiver operating characteristic","level":2,"score":0.4383629858493805},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4066488742828369},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3741946816444397},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3508091866970062},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.29920756816864014}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2022.3212067","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3212067","pdf_url":null,"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:3df6fd4dfebf488cb4e145af559d6085","is_oa":true,"landing_page_url":"https://doaj.org/article/3df6fd4dfebf488cb4e145af559d6085","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":"IEEE Access, Vol 10, Pp 107459-107476 (2022)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2022.3212067","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3212067","pdf_url":null,"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":0.5299999713897705,"id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":61,"referenced_works":["https://openalex.org/W807187018","https://openalex.org/W1501181189","https://openalex.org/W1985419027","https://openalex.org/W2016944307","https://openalex.org/W2068636224","https://openalex.org/W2070465052","https://openalex.org/W2070879808","https://openalex.org/W2093836791","https://openalex.org/W2184674722","https://openalex.org/W2204950599","https://openalex.org/W2236731268","https://openalex.org/W2242109922","https://openalex.org/W2316700091","https://openalex.org/W2472634428","https://openalex.org/W2620166702","https://openalex.org/W2752627726","https://openalex.org/W2753434909","https://openalex.org/W2756848027","https://openalex.org/W2759240221","https://openalex.org/W2766296277","https://openalex.org/W2807027008","https://openalex.org/W2900329012","https://openalex.org/W2908736795","https://openalex.org/W2919115771","https://openalex.org/W2924713973","https://openalex.org/W2932881901","https://openalex.org/W2936503027","https://openalex.org/W2946660569","https://openalex.org/W2961840197","https://openalex.org/W2964077297","https://openalex.org/W2985328577","https://openalex.org/W2990991805","https://openalex.org/W2999483804","https://openalex.org/W3034614422","https://openalex.org/W3047276250","https://openalex.org/W3080272905","https://openalex.org/W3093293285","https://openalex.org/W3097188514","https://openalex.org/W3126647681","https://openalex.org/W3129619312","https://openalex.org/W3130449168","https://openalex.org/W3135225825","https://openalex.org/W3156485131","https://openalex.org/W3167375992","https://openalex.org/W3189035319","https://openalex.org/W3214439486","https://openalex.org/W4200122681","https://openalex.org/W4205970524","https://openalex.org/W4206081943","https://openalex.org/W4206627303","https://openalex.org/W4207003862","https://openalex.org/W4211058450","https://openalex.org/W4220755962","https://openalex.org/W4220814249","https://openalex.org/W4226239514","https://openalex.org/W4226464009","https://openalex.org/W4232215346","https://openalex.org/W6607259140","https://openalex.org/W6686309426","https://openalex.org/W6699742216","https://openalex.org/W6785183096"],"related_works":["https://openalex.org/W1774890144","https://openalex.org/W4293426625","https://openalex.org/W2728311169","https://openalex.org/W4318621097","https://openalex.org/W2988362112","https://openalex.org/W2057416691","https://openalex.org/W2789171106","https://openalex.org/W2776262946","https://openalex.org/W1937501893","https://openalex.org/W3189944972"],"abstract_inverted_index":{"Diabetes":[0,58],"has":[1],"become":[2],"one":[3],"of":[4,15,26,36,71,111,134,160,189,198,215,227,252],"the":[5,16,69,95,100,106,115,131,156,172,175,187,190,201,213,224,241,245,250],"seven":[6],"major":[7],"diseases":[8],"affecting":[9],"human":[10],"death,":[11],"so":[12],"early":[13],"prediction":[14],"disease":[17],"to":[18,92,104,113,126,170,185,211,264],"prevent":[19],"it":[20],"is":[21,90,124,162,230,237],"critical.":[22],"Several":[23],"existing":[24],"works":[25],"literature,":[27],"however,":[28],"make":[29],"predictions":[30],"about":[31],"diabetes":[32,112,167,180,193,253,266],"with":[33,99],"few":[34],"considerations":[35],"missing":[37,72,96],"and":[38,74,108,144,183,255],"imbalanced":[39],"data":[40],"proper.":[41],"To":[42],"overcome":[43],"these":[44],"problems,":[45],"in":[46,192,196,260],"this":[47,228],"paper,":[48],"we":[49],"propose":[50],"an":[51],"efficient":[52],"Probabilistic":[53],"Ensemble":[54],"classification":[55,76,158,217],"algorithm":[56],"for":[57,94],"handling":[59],"class":[60],"Imbalance":[61],"Missing":[62],"values":[63],"(PE_DIM)":[64],"which":[65,236],"can":[66],"effectively":[67,248],"handle":[68],"issue":[70],"imbalances":[73],"improve":[75,265],"accuracy.":[77],"First,":[78],"a":[79,120,257],"novel":[80],"method":[81,103,191,229,247],"based":[82],"on":[83,130,164],"Local":[84],"Median-based":[85],"Gaussian":[86],"Naive":[87],"Bayes":[88],"(LMeGNB)":[89],"proposed":[91,246],"compensate":[93],"values,":[97],"combined":[98],"K-means":[101],"SMOTE":[102],"adjust":[105],"positive":[107],"negative":[109],"samples":[110],"obtain":[114],"normalized":[116],"balanced":[117],"data.":[118],"Then,":[119],"probability-based":[121],"multi-stage":[122],"ensemble":[123,128],"devoted":[125],"building":[127],"models":[129],"different":[132,216,239],"types":[133],"machine":[135],"learning":[136],"algorithms.":[137],"When":[138],"extreme":[139],"gradient":[140],"boosting,":[141],"random":[142],"forests,":[143],"weighted":[145],"<inline-formula":[146],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[147],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">":[148],"<tex-math":[149],"notation=\"LaTeX\">$k$":[150],"</tex-math></inline-formula>":[151],"nearest":[152],"neighbors":[153],"are":[154],"integrated,":[155],"highest":[157],"accuracy":[159],"94.53%":[161],"obtained":[163],"Pima":[165],"Indian":[166],"dataset.":[168],"Finally,":[169],"evaluate":[171],"PE_DIM":[173],"model,":[174],"experiment":[176],"equally":[177],"considered":[178],"two":[179],"datasets,":[181],"RSMH":[182],"Tabriz,":[184],"demonstrate":[186,222],"generality":[188],"prediction.":[194],"Additionally,":[195],"terms":[197],"area":[199],"under":[200],"receiver":[202],"operating":[203],"characteristic":[204],"curve":[205],"metric":[206],"uses":[207],"several":[208],"statistical":[209],"tests":[210],"measure":[212],"performance":[214],"methods.":[218],"The":[219],"ultimate":[220],"results":[221],"that":[223],"average":[225],"rank":[226],"ranked":[231],"first":[232],"after":[233],"5-fold":[234],"cross-validation,":[235],"significantly":[238],"from":[240],"basic":[242],"classifiers.":[243],"Promisingly,":[244],"solves":[249],"lack":[251],"imbalance":[254],"plays":[256],"significant":[258],"role":[259],"intelligent":[261],"medical":[262],"treatment":[263],"research.":[267]},"counts_by_year":[{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":6}],"updated_date":"2026-03-10T16:38:18.471706","created_date":"2023-01-05T00:00:00"}
