{"id":"https://openalex.org/W4307136392","doi":"https://doi.org/10.1155/2022/9749579","title":"Predictive Model for Diagnosis of Gestational Diabetes in the Kurdistan Region by a Combination of Clustering and Classification Algorithms: An Ensemble Approach","display_name":"Predictive Model for Diagnosis of Gestational Diabetes in the Kurdistan Region by a Combination of Clustering and Classification Algorithms: An Ensemble Approach","publication_year":2022,"publication_date":"2022-10-22","ids":{"openalex":"https://openalex.org/W4307136392","doi":"https://doi.org/10.1155/2022/9749579"},"language":"en","primary_location":{"id":"doi:10.1155/2022/9749579","is_oa":true,"landing_page_url":"https://doi.org/10.1155/2022/9749579","pdf_url":"https://downloads.hindawi.com/journals/acisc/2022/9749579.pdf","source":{"id":"https://openalex.org/S30680879","display_name":"Applied Computational Intelligence and Soft Computing","issn_l":"1687-9724","issn":["1687-9724","1687-9732"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319869","host_organization_name":"Hindawi Publishing Corporation","host_organization_lineage":["https://openalex.org/P4310319869"],"host_organization_lineage_names":["Hindawi Publishing Corporation"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Computational Intelligence and Soft Computing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://downloads.hindawi.com/journals/acisc/2022/9749579.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5043865779","display_name":"Rasool F. Jader","orcid":"https://orcid.org/0000-0003-4563-4050"},"institutions":[{"id":"https://openalex.org/I32706565","display_name":"Soran University","ror":"https://ror.org/03k9q0e81","country_code":"IQ","type":"education","lineage":["https://openalex.org/I32706565"]}],"countries":["IQ"],"is_corresponding":false,"raw_author_name":"Rasool Jader","raw_affiliation_strings":["Computer Science, Faculty of Science, Soran University, Soran 44008, Kurdistan, Iraq"],"affiliations":[{"raw_affiliation_string":"Computer Science, Faculty of Science, Soran University, Soran 44008, Kurdistan, Iraq","institution_ids":["https://openalex.org/I32706565"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5037392327","display_name":"Sadegh Aminifar","orcid":"https://orcid.org/0000-0003-0525-6163"},"institutions":[{"id":"https://openalex.org/I32706565","display_name":"Soran University","ror":"https://ror.org/03k9q0e81","country_code":"IQ","type":"education","lineage":["https://openalex.org/I32706565"]}],"countries":["IQ"],"is_corresponding":true,"raw_author_name":"Sadegh Aminifar","raw_affiliation_strings":["Computer Science, Faculty of Science, Soran University, Soran 44008, Kurdistan, Iraq"],"affiliations":[{"raw_affiliation_string":"Computer Science, Faculty of Science, Soran University, Soran 44008, Kurdistan, Iraq","institution_ids":["https://openalex.org/I32706565"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5037392327"],"corresponding_institution_ids":["https://openalex.org/I32706565"],"apc_list":{"value":900,"currency":"USD","value_usd":900},"apc_paid":{"value":900,"currency":"USD","value_usd":900},"fwci":8.6938,"has_fulltext":true,"cited_by_count":37,"citation_normalized_percentile":{"value":0.9763845,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":"2022","issue":null,"first_page":"1","last_page":"11"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.9983999729156494,"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":0.9983999729156494,"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":0.9664999842643738,"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/T10538","display_name":"Data Mining Algorithms and Applications","score":0.946399986743927,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/k-means-clustering","display_name":"k-means clustering","score":0.7481685280799866},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.7380063533782959},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7113322019577026},{"id":"https://openalex.org/keywords/mahalanobis-distance","display_name":"Mahalanobis distance","score":0.6938462257385254},{"id":"https://openalex.org/keywords/gestational-diabetes","display_name":"Gestational diabetes","score":0.6054211258888245},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.5582552552223206},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.5311176776885986},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4833635687828064},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.4648740589618683},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4592883288860321},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.4225972592830658},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3933713734149933},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.38311219215393066},{"id":"https://openalex.org/keywords/pregnancy","display_name":"Pregnancy","score":0.3561726212501526},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.34078335762023926},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3242247700691223},{"id":"https://openalex.org/keywords/gestation","display_name":"Gestation","score":0.12065848708152771}],"concepts":[{"id":"https://openalex.org/C207968372","wikidata":"https://www.wikidata.org/wiki/Q310401","display_name":"k-means clustering","level":3,"score":0.7481685280799866},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7380063533782959},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7113322019577026},{"id":"https://openalex.org/C1921717","wikidata":"https://www.wikidata.org/wiki/Q1334846","display_name":"Mahalanobis distance","level":2,"score":0.6938462257385254},{"id":"https://openalex.org/C2779434492","wikidata":"https://www.wikidata.org/wiki/Q126691","display_name":"Gestational diabetes","level":4,"score":0.6054211258888245},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.5582552552223206},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.5311176776885986},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4833635687828064},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.4648740589618683},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4592883288860321},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.4225972592830658},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3933713734149933},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38311219215393066},{"id":"https://openalex.org/C2779234561","wikidata":"https://www.wikidata.org/wiki/Q11995","display_name":"Pregnancy","level":2,"score":0.3561726212501526},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.34078335762023926},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3242247700691223},{"id":"https://openalex.org/C46973012","wikidata":"https://www.wikidata.org/wiki/Q28627","display_name":"Gestation","level":3,"score":0.12065848708152771},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1155/2022/9749579","is_oa":true,"landing_page_url":"https://doi.org/10.1155/2022/9749579","pdf_url":"https://downloads.hindawi.com/journals/acisc/2022/9749579.pdf","source":{"id":"https://openalex.org/S30680879","display_name":"Applied Computational Intelligence and Soft Computing","issn_l":"1687-9724","issn":["1687-9724","1687-9732"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319869","host_organization_name":"Hindawi Publishing Corporation","host_organization_lineage":["https://openalex.org/P4310319869"],"host_organization_lineage_names":["Hindawi Publishing Corporation"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Computational Intelligence and Soft Computing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:2a50430ed5974d20a17d5241683e2909","is_oa":true,"landing_page_url":"https://doaj.org/article/2a50430ed5974d20a17d5241683e2909","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":"Applied Computational Intelligence and Soft Computing, Vol 2022 (2022)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1155/2022/9749579","is_oa":true,"landing_page_url":"https://doi.org/10.1155/2022/9749579","pdf_url":"https://downloads.hindawi.com/journals/acisc/2022/9749579.pdf","source":{"id":"https://openalex.org/S30680879","display_name":"Applied Computational Intelligence and Soft Computing","issn_l":"1687-9724","issn":["1687-9724","1687-9732"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319869","host_organization_name":"Hindawi Publishing Corporation","host_organization_lineage":["https://openalex.org/P4310319869"],"host_organization_lineage_names":["Hindawi Publishing Corporation"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Computational Intelligence and Soft Computing","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being","score":0.8700000047683716}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4307136392.pdf","grobid_xml":"https://content.openalex.org/works/W4307136392.grobid-xml"},"referenced_works_count":26,"referenced_works":["https://openalex.org/W139010480","https://openalex.org/W316696310","https://openalex.org/W2016048214","https://openalex.org/W2024155246","https://openalex.org/W2050520607","https://openalex.org/W2118414527","https://openalex.org/W2188383613","https://openalex.org/W2443862987","https://openalex.org/W2664267452","https://openalex.org/W2889751329","https://openalex.org/W2938029723","https://openalex.org/W2946663007","https://openalex.org/W2953421802","https://openalex.org/W2970642414","https://openalex.org/W2978631110","https://openalex.org/W3002843673","https://openalex.org/W3003647881","https://openalex.org/W3008233702","https://openalex.org/W3019913914","https://openalex.org/W3040703651","https://openalex.org/W3098522193","https://openalex.org/W3116062483","https://openalex.org/W4205558929","https://openalex.org/W4205888351","https://openalex.org/W4282006100","https://openalex.org/W4295837121"],"related_works":["https://openalex.org/W4382795578","https://openalex.org/W4389954502","https://openalex.org/W2771255398","https://openalex.org/W2930428186","https://openalex.org/W3200027047","https://openalex.org/W4385770464","https://openalex.org/W3125536479","https://openalex.org/W3120363735","https://openalex.org/W4214820172","https://openalex.org/W2394323384"],"abstract_inverted_index":{"Gestational":[0],"diabetes":[1],"is":[2],"a":[3,80,167],"type":[4],"of":[5,19,54,70,169],"high":[6],"blood":[7],"sugar":[8],"that":[9,165],"develops":[10],"during":[11,31],"pregnancy.":[12],"It":[13],"can":[14,37],"occur":[15],"at":[16],"any":[17],"stage":[18],"pregnancy":[20],"and":[21,28,32,45,104,118,128,141,155,176],"cause":[22],"problems":[23],"for":[24,115,160],"both":[25],"the":[26,29,74,93,111,119,124,129,142],"mother":[27],"baby,":[30],"after":[33],"birth.":[34],"The":[35,88,107,162],"risks":[36],"be":[38],"reduced":[39],"if":[40],"they":[41],"are":[42,57,66,158],"early":[43],"detected":[44],"managed,":[46],"especially":[47],"in":[48],"areas":[49],"where":[50],"only":[51],"periodic":[52],"tests":[53],"pregnant":[55,101],"women":[56,102],"available.":[58],"Intelligent":[59],"systems":[60],"designed":[61],"by":[62],"machine":[63],"learning":[64],"algorithms":[65],"remodelling":[67],"all":[68],"fields":[69],"our":[71],"lives,":[72],"including":[73],"healthcare":[75],"system.":[76],"This":[77],"study":[78],"proposes":[79],"combined":[81],"prediction":[82,181],"model":[83,109],"to":[84,122,133,138],"diagnose":[85],"gestational":[86],"diabetes.":[87,106],"dataset":[89],"was":[90],"obtained":[91],"from":[92,100],"Kurdistan":[94],"region":[95],"laboratories,":[96],"which":[97],"collected":[98],"information":[99],"with":[103],"without":[105],"suggested":[108],"uses":[110],"clustering":[112],"KMeans":[113,170],"technique":[114,178],"data":[116],"reduction":[117],"elbow":[120,172],"method":[121,132],"find":[123,134],"optimal":[125],"k":[126],"value":[127],"Mahalanobis":[130,174],"distance":[131],"more":[135],"related":[136],"cluster":[137],"new":[139],"samples,":[140],"classification":[143],"methods":[144],"such":[145],"as":[146],"decision":[147],"tree,":[148],"random":[149],"forest,":[150],"SVM,":[151],"KNN,":[152],"logistic":[153],"regression,":[154],"Na\u00efve":[156],"Bayes":[157],"used":[159],"prediction.":[161],"results":[163],"showed":[164],"using":[166],"mix":[168],"clustering,":[171],"method,":[173],"distance,":[175],"ensemble":[177],"significantly":[179],"improves":[180],"accuracy.":[182]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":12},{"year":2024,"cited_by_count":11},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":2}],"updated_date":"2026-02-27T16:54:17.756197","created_date":"2025-10-10T00:00:00"}