{"id":"https://openalex.org/W4408995301","doi":"https://doi.org/10.1049/icp.2025.0925","title":"Supervised machine learning-based comparative study for diabetes prediction with performance tuning","display_name":"Supervised machine learning-based comparative study for diabetes prediction with performance tuning","publication_year":2025,"publication_date":"2025-03-01","ids":{"openalex":"https://openalex.org/W4408995301","doi":"https://doi.org/10.1049/icp.2025.0925"},"language":"en","primary_location":{"id":"doi:10.1049/icp.2025.0925","doi":"https://doi.org/10.1049/icp.2025.0925","is_oa":false,"landing_page_url":"https://doi.org/10.1049/icp.2025.0925","pdf_url":null,"source":{"id":"https://openalex.org/S4404663355","display_name":"IET conference proceedings.","issn_l":"2732-4494","issn":["2732-4494"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":["jufo-1"],"host_organization":"https://openalex.org/P4310311714","host_organization_name":"Institution of Engineering and Technology","host_organization_lineage":["https://openalex.org/P4310311714"],"host_organization_lineage_names":["Institution of Engineering and Technology"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IET Conference Proceedings","raw_type":"journal-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://hdl.handle.net/11508/41607","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5007411152","display_name":"Necip \u00c7\u0131nar","orcid":"https://orcid.org/0000-0002-5106-6240","observed_orcids":["https://orcid.org/0000-0002-5106-6240"]},"institutions":[{"id":"https://openalex.org/I123466251","display_name":"Dicle University","ror":"https://ror.org/0257dtg16","country_code":"TR","type":"education","lineage":["https://openalex.org/I123466251"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Necip \u00c7\u0131nar","raw_affiliation_strings":["Department of Computer Engineering, Dicle University, Diyabak\u0131r, Turkey"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Engineering, Dicle University, Diyabak\u0131r, Turkey","institution_ids":["https://openalex.org/I123466251"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100631239","display_name":"Mehmet Kaya","orcid":"https://orcid.org/0000-0003-2995-8282","observed_orcids":["https://orcid.org/0000-0003-2995-8282"]},"institutions":[{"id":"https://openalex.org/I143396566","display_name":"F\u0131rat University","ror":"https://ror.org/05teb7b63","country_code":"TR","type":"education","lineage":["https://openalex.org/I143396566"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Mehmet Kaya","raw_affiliation_strings":["Department of Computer Engineering, F\u0131rat University, Elaz\u0131\u011f, Turkey"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Engineering, F\u0131rat University, Elaz\u0131\u011f, Turkey","institution_ids":["https://openalex.org/I143396566"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"2024","issue":"37","first_page":"292","last_page":"297"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.9990000128746033,"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":0.9990000128746033,"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/T13373","display_name":"Data Mining and Machine Learning Applications","score":0.00019999999494757503,"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"}},{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":9.999999747378752e-05,"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/diabetes-prediction","display_name":"diabetes prediction","score":0.9909999966621399},{"id":"https://openalex.org/keywords/diabetes-diagnosis","display_name":"diabetes diagnosis","score":0.8740000128746033},{"id":"https://openalex.org/keywords/pima-indians-diabetes-dataset","display_name":"Pima Indians diabetes dataset","score":0.8240000009536743},{"id":"https://openalex.org/keywords/hyperparameter-optimization","display_name":"hyperparameter optimization","score":0.9300000071525574},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"gradient boosting","score":0.7310000061988831},{"id":"https://openalex.org/keywords/xgboost","display_name":"XGBoost","score":0.9810000061988831},{"id":"https://openalex.org/keywords/random-forest","display_name":"random forest","score":0.828000009059906},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"support vector machine","score":0.8429999947547913},{"id":"https://openalex.org/keywords/k-nearest-neighbors","display_name":"k-nearest neighbors","score":0.7620000243186951},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"logistic regression","score":0.859000027179718},{"id":"https://openalex.org/keywords/decision-tree","display_name":"decision tree","score":0.8769999742507935},{"id":"https://openalex.org/keywords/model-comparison","display_name":"model comparison","score":0.6660000085830688},{"id":"https://openalex.org/keywords/medical-data-analysis","display_name":"medical data analysis","score":0.5210000276565552}],"concepts":[{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6529927253723145},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6305069923400879},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6032869815826416}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1049/icp.2025.0925","doi":"https://doi.org/10.1049/icp.2025.0925","is_oa":false,"landing_page_url":"https://doi.org/10.1049/icp.2025.0925","pdf_url":null,"source":{"id":"https://openalex.org/S4404663355","display_name":"IET conference proceedings.","issn_l":"2732-4494","issn":["2732-4494"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":["jufo-1"],"host_organization":"https://openalex.org/P4310311714","host_organization_name":"Institution of Engineering and Technology","host_organization_lineage":["https://openalex.org/P4310311714"],"host_organization_lineage_names":["Institution of Engineering and Technology"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IET Conference Proceedings","raw_type":"journal-article"},{"id":"pmh:oai:openaccess.firat.edu.tr:11508/41607","doi":null,"is_oa":true,"landing_page_url":"https://hdl.handle.net/11508/41607","pdf_url":null,"source":{"id":"https://openalex.org/S4377196733","display_name":"DSpace - FIRAT (F\u0131rat University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I143396566","host_organization_name":"F\u0131rat University","host_organization_lineage":["https://openalex.org/I143396566"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Conference Object"}],"best_oa_location":{"id":"pmh:oai:openaccess.firat.edu.tr:11508/41607","doi":null,"is_oa":true,"landing_page_url":"https://hdl.handle.net/11508/41607","pdf_url":null,"source":{"id":"https://openalex.org/S4377196733","display_name":"DSpace - FIRAT (F\u0131rat University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I143396566","host_organization_name":"F\u0131rat University","host_organization_lineage":["https://openalex.org/I143396566"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Conference Object"},"sustainable_development_goals":[{"score":0.9476000070571899,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7220700408","https://openalex.org/W4412860310","https://openalex.org/W4381737452","https://openalex.org/W4392893517","https://openalex.org/W4404688901","https://openalex.org/W4410705973","https://openalex.org/W7081953295","https://openalex.org/W4399951303","https://openalex.org/W7160411614","https://openalex.org/W4402318835"],"abstract_inverted_index":{"Diabetes":[0],"is":[1,5,11,30],"a":[2],"condition":[3],"that":[4],"gravely":[6],"affecting":[7],"people's":[8],"health":[9],"and":[10,28,41,102,122,144,167,174,188,201,219,226],"spreading":[12],"quickly":[13],"throughout":[14],"the":[15,33,48,59,63,77,103,113,124,153,161,179,198,202,209,213],"world.":[16],"The":[17,39,147,192],"necessity":[18],"for":[19,75,86,126,160,212],"scientific":[20],"research":[21],"on":[22,108],"diabetes":[23,37,87],"prevention,":[24],"early":[25],"diagnosis,":[26],"treatment,":[27],"follow-up":[29],"highlighted":[31],"by":[32,47,54],"growing":[34],"number":[35],"of":[36,43,50,65,105,163,215],"patients.":[38],"diagnosis":[40],"treatment":[42],"diseases":[44],"are":[45,68],"improved":[46],"analysis":[49],"data":[51,116],"made":[52],"possible":[53],"current":[55],"technology":[56],"advancements":[57],"in":[58,89],"medical":[60],"industry.":[61],"Within":[62],"context":[64],"diabetes,":[66],"scientists":[67],"working":[69],"to":[70,91,120],"provide":[71],"systematic,":[72],"data-based":[73],"methods":[74,125],"diagnosing":[76],"condition.":[78],"This":[79],"study":[80],"examines":[81],"popular":[82],"machine":[83],"learning":[84],"models":[85],"detection":[88],"order":[90],"achieve":[92],"this":[93,158],"goal.":[94],"Concurrently,":[95],"these":[96,106],"models'":[97,183,193],"hyper":[98,184],"parameters":[99,185],"were":[100,170,186,222],"adjusted,":[101],"impact":[104],"adjustments":[107],"performance":[109],"was":[110,118],"examined.":[111],"First,":[112],"Pima":[114],"Indians":[115],"set":[117],"used":[119],"train":[121],"compare":[123],"Logistic":[127],"Regression,":[128],"K":[129],"Neighbors":[130],"Classifier,":[131,134,137,143],"Decision":[132],"Tree":[133],"Random":[135],"Forest":[136],"Support":[138],"Vector":[139],"Machine,":[140],"Gradient":[141,148,203],"Boosting":[142,149,204],"LGBM":[145],"Classifier.":[146],"(XGB)":[150],"algorithm":[151],"produced":[152,208],"highest":[154],"accuracy":[155],"values":[156,211],"at":[157],"point":[159],"metrics":[162,214],"accuracy,":[164,216],"recall,":[165,217],"precision,":[166,218],"f1-score,":[168,220],"which":[169,221],"91.03%,":[171],"91.83%,":[172],"91.77%,":[173],"91.14%,":[175],"respectively.":[176,228],"Next,":[177],"using":[178],"same":[180],"dataset,":[181],"all":[182],"adjusted":[187],"trained":[189],"once":[190,206],"more.":[191],"success":[194],"increased":[195],"significantly":[196],"after":[197],"tuning":[199],"procedure,":[200],"approach":[205],"more":[207],"greatest":[210],"93.46%,":[223],"93.71%,":[224],"93.96%,":[225],"93.08,":[227]},"counts_by_year":[],"updated_date":"2026-10-15T10:00:46.086208","created_date":"2025-03-31T00:00:00"}